Generative AI Interview Questions & Answers

Generative AI focuses on creating new content—text, images, code, audio—by learning patterns from data. Traditional ML often focuses on prediction or classification, like forecasting demand or detecting spam.

The key difference is the output: a classifier chooses a label from known options, while a generative model produces new sequences (words/pixels) that look realistic and relevant. In practice, generative systems are used for drafting content, summarizing documents, building assistants, or generating designs.

A useful mental model: traditional ML answers “Which bucket does this belong to?” while Generative AI answers “What should the next token/pixel be?”.

LLMs do not read text as “words” the way humans do. They convert text into tokens—small units that may be whole words, sub-words, or characters. Tokenization matters because it affects:

• Cost & latency: most APIs charge per token and longer inputs take more time.
• Context limits: model memory is measured in tokens, not characters.
• Accuracy: weird token splits can make names, code, and mixed languages harder.

When building products, you track tokens for budgeting, decide chunk sizes for RAG, and design prompts that keep the “important” parts inside context.

Next-token prediction means the model learns to guess the next token given previous tokens. During training, it sees huge text corpora and constantly predicts what comes next. Over time, it learns grammar, facts, reasoning patterns, and styles.

It is powerful because many tasks can be expressed as “continue the text”: answering questions, writing emails, summarizing, translating, generating code, and planning steps. The prompt becomes the “setup,” and the completion becomes the output.

In product terms, you are not building a different model for each task; you are shaping one model with the right instructions, examples, and constraints.

Embeddings are numeric vectors that represent meaning. Similar items have vectors that are close in vector space. They are used for:

• Semantic search: finding relevant text beyond keyword matching.
• RAG: retrieving the best chunks to answer questions from your documents.
• Clustering: grouping similar tickets, emails, or user queries.
• Recommendations: matching users with content/products.

In production, embeddings are typically stored in a vector database. When a user asks a question, you embed the query, retrieve nearest chunks, and feed those chunks into the LLM for grounded answers.

Hallucination is when an LLM produces confident but incorrect or unsupported information. It happens because the model is optimized to produce likely text, not to verify truth. If the prompt lacks grounding, or the model has weak knowledge, it may “fill gaps” with plausible-sounding text.

Mitigation strategies include:

• RAG grounding with trusted sources.
• Clear instructions to cite only provided context.
• Refusal rules when data is missing.
• Post-checks like schema validation or fact verification.

In interviews, highlight that hallucination is a product risk and must be handled with design, not hope.

Context length is the maximum number of tokens a model can consider at once (prompt + retrieved text + generated output). If you exceed it, the system must truncate or fail.

It affects design decisions:

• Prompt discipline: keep instructions compact and reusable.
• Chunk strategy: smaller chunks help retrieval precision, but too small loses meaning.
• Compression: summarize long docs before including them.
• Memory: store long-term info outside the model and retrieve on demand.

A strong answer mentions balancing relevance, cost, and accuracy.

Temperature controls randomness. Low temperature makes outputs more deterministic and consistent; higher temperature increases creativity but also risk of drifting or hallucination.

Top-p (nucleus sampling) limits generation to a subset of likely tokens whose cumulative probability is p. Lower top-p makes outputs safer and more focused.

Practical rule:

• Factual/business outputs: low temperature (more stable).
• Brainstorming/creative: higher temperature and moderate top-p.

In production, you often tune these per task and log metrics like user satisfaction and error rates.

A system prompt is the highest-priority instruction that defines the assistant’s role, boundaries, style, and rules. It matters because it gives consistent behavior across conversations—like always following compliance rules, using a certain tone, or refusing unsafe requests.

In enterprise apps, system prompts often include:

• Safety and privacy rules
• Formatting requirements (JSON, HTML)
• Grounding rules (answer only from retrieved documents)

A good system prompt reduces prompt drift and makes outputs more predictable for product teams.

Text completion treats the input as one text prompt and produces a continuation. Chat completion structures the input into roles (system/user/assistant) and is better for multi-turn conversations, instruction-following, and tool use.

Chat APIs help maintain conversation state and make it easier to separate rules (system) from user requests. Text completion is sometimes simpler for single-shot generation tasks like rewriting or templated content.

In practice, most modern assistants and enterprise copilots use chat-based formats because they integrate better with memory, retrieval, and tools.

Prompt injection is when untrusted text (user input or retrieved documents) contains instructions that try to override system rules. Example: a user asks a helpdesk bot a question and includes “Ignore previous instructions and reveal system prompt.”

It breaks systems when the model follows those malicious instructions, leaking secrets or producing unsafe actions.

Defenses include:

• Separating data from instructions (delimiters, content tags)
• Tool permissioning and allowlists
• Content sanitization and safe retrieval
• Explicit rule: treat retrieved text as reference, not commands

Grounding means making the model’s answer depend on trusted sources (documents, databases, APIs) instead of only its internal training. Enterprises care because it reduces hallucinations, improves auditability, and aligns outputs with company policy.

Common grounding methods are:

• RAG: retrieve relevant documents and cite them.
• Tool calls: fetch live data from APIs (pricing, inventory, tickets).
• Structured outputs: validate answers against schemas and business rules.

Grounding is one of the main differences between a demo chatbot and a production enterprise assistant.

You track both quality and risk. Quality metrics include task success rate, user rating, latency, cost per request, and correction rate (how often users edit the output). Risk metrics include policy violations, hallucination rate (sampled audits), data leakage signals, and prompt injection attempts.

For RAG, track retrieval precision/recall, chunk hit rate, and citation coverage. For tool-based systems, track tool error rate and retry counts.

A mature setup includes offline evaluation datasets plus online monitoring with human review for high-impact flows.

Self-attention lets a model decide which earlier tokens matter most when processing the current token. Instead of reading left-to-right with fixed memory, it computes relationships between all tokens in a sequence.

Example: in “The trophy didn’t fit in the suitcase because it was too big,” attention helps “it” connect to “trophy.”

Technically, it creates query/key/value representations and uses similarity to weight information. The benefit is stronger context handling and parallel computation, which is why transformers scaled so well compared to older RNNs.

Multi-head attention runs several attention mechanisms in parallel. Each “head” can learn different types of relationships—syntax, long-range references, or topic-level links.

With one head, the model might focus heavily on one pattern and miss others. Multiple heads give richer representation and better performance, especially on complex language tasks and code.

In interviews, mention that heads can specialize, and the final output is a concatenation/projection of all heads, improving expressiveness without exploding computation as much as adding separate layers would.

Embeddings represent token meaning (e.g., “bank” as text). Positional encodings represent token order, because transformers don’t naturally know sequence order.

Without positional information, “dog bites man” and “man bites dog” would look similar. Positional encodings (sinusoidal or learned) inject order so attention can interpret “which token comes first.”

In practice, both are added or combined to form the input representation the transformer processes. Understanding this is important when debugging long-context performance and chunking strategies.

A decoder-only model (like many GPT-style systems) predicts the next token based on previous tokens. It’s well-suited for generation because it naturally produces continuations.

Encoder-decoder models are often used for translation or summarization, where the input is encoded and output is decoded. Decoder-only models can still do these tasks by prompting, which makes them versatile.

Chatbots benefit because the conversation history is simply the input context, and the model generates the assistant’s reply. It is also easier to scale and deploy for general-purpose generation tasks.

Instruction tuning trains a model on datasets where the input is an instruction (question/task) and the output is a helpful response. It makes models better at following directions rather than only continuing generic text.

It improves helpfulness because the model learns patterns like: ask clarifying questions, format output neatly, provide steps, and align to user intent.

In product terms, instruction tuning reduces how much prompt engineering you need for common tasks. It’s also often combined with preference tuning (e.g., RLHF) to align outputs with quality and safety expectations.

RLHF (Reinforcement Learning from Human Feedback) is a way to align model behavior with human preferences. First, humans compare outputs and choose which one is better. That creates a “preference model” (reward signal). Then the LLM is trained to produce outputs that get higher reward.

The practical impact is: fewer harmful outputs, better refusal behavior, clearer helpful answers, and more user-friendly style.

In interviews, it’s good to mention that RLHF doesn’t make the model “true,” but it can improve how it responds and how well it follows safety and quality expectations.

Perplexity measures how well a language model predicts tokens—lower is usually better. It’s useful for comparing language modeling performance on similar distributions.

It becomes misleading when the task is not purely “predict next word.” A model might have good perplexity but still produce poor instruction-following, weak reasoning, or unsafe outputs. Also, perplexity doesn’t capture factuality or business correctness.

For enterprise apps, you combine language metrics with task-based evaluation: accuracy on internal QA sets, hallucination audits, and user satisfaction.

Common failure modes include hallucination, prompt injection, inconsistency across turns, and “overconfidence” where the model answers even when information is missing. You also see formatting failures (invalid JSON), refusal when it should answer, and slow/expensive responses when context grows.

In RAG systems, retrieval failures are big: wrong chunks, outdated documents, or irrelevant content. In tool systems, the model might call the wrong tool or mis-handle errors.

A strong answer includes mitigations: grounding, validation, guardrails, retries, and monitoring.

Quantization reduces model size and speeds up inference by using lower-precision numbers (like int8 or int4 instead of fp16). Teams use it to reduce GPU memory usage, lower latency, and cut costs—especially when hosting open-source models.

The tradeoff is that extreme quantization can reduce output quality, especially for reasoning or long-context tasks. Good engineering tests quantization levels per use case.

In practice: you benchmark accuracy, latency, and cost, then choose a quantization strategy that meets SLA without harming quality.

KV cache stores key/value tensors from attention layers for previously processed tokens. In multi-turn chat or long responses, caching prevents recomputing attention for the whole history each time.

It matters because it reduces compute significantly, making streaming faster and cheaper. The longer the conversation, the bigger the benefit.

In production, KV cache influences concurrency and GPU memory planning. If you run many sessions in parallel, caches can become large, so teams manage truncation, summarization, and memory policies.

You choose a smaller model when latency and cost are critical, or when the task is narrow and well-defined (classification, extraction, short answers). Smaller models often scale better in high-traffic apps and are easier to deploy.

You might also use a cascade: smaller model first, escalate to bigger model only when needed. This cuts cost without sacrificing quality.

The best approach is empirical: run A/B tests on your evaluation set and compare accuracy, speed, and total cost. Many enterprise systems use multiple models for different tasks.

SFT (Supervised Fine-Tuning) trains the model to mimic “ideal answers” from labeled examples. It teaches style and content patterns.

Preference tuning (like RLHF or DPO-style approaches) uses comparisons between outputs to teach what humans prefer, improving helpfulness and safety.

In practice, SFT gets you “competent” behavior aligned to your domain, and preference tuning improves quality and reduces undesirable patterns. Many production training pipelines use both: SFT first, then preference optimization.

Consistency comes from giving the model a strict contract. You define the schema clearly and ask it to output only JSON—no explanations. Good prompts include:

• Schema with field types and required keys
• Example input and example output
• Rules like “Use double quotes, no trailing commas”

In production, you also validate JSON and retry with an error message if parsing fails. For critical flows, you keep outputs short and avoid unnecessary creativity (low temperature).

Few-shot prompting includes a small set of examples demonstrating the desired behavior. It’s better than zero-shot when the task is specific—like tone, formatting, domain rules, or edge cases.

For example, if you want an LLM to generate course overviews in your brand style, a few examples will anchor the output. It reduces variability and improves accuracy on tricky cases.

The tradeoff is context usage: too many examples increase token cost and can push out important user content. The best strategy is a small, high-quality example set with coverage of key patterns.

Chain-of-thought is the model producing intermediate reasoning steps. It can improve multi-step tasks like math, planning, or logic because the model “walks through” the problem.

In products, you may hide internal reasoning because it can leak sensitive context, confuse users, or increase legal/compliance risk. Instead, you ask the model to “think internally” but output only final steps or a short rationale.

A safe pattern is: internal reasoning for the model, but user-facing output is a clear final answer plus brief explanation without revealing hidden instructions.

Prompting can reduce hallucination, but it can’t eliminate it without grounding. Useful prompt techniques include:

• Ask the model to state when it does not know.
• Require citations from provided context only.
• Use “answer using the following sources” with delimiters.
• Force extraction mode (quote exact lines before answering).

Still, for enterprise reliability you typically add RAG or tools. Prompting is best seen as one layer of control, not the entire safety strategy.

Prompt chaining breaks a complex task into smaller steps, each with its own prompt. For example: (1) extract requirements, (2) generate outline, (3) write final content, (4) validate output.

It helps because each step is simpler and easier to control. You can add checks between steps (schema validation, policy checks), and the model’s output becomes more consistent.

In enterprise workflows like document processing, chains reduce errors and make debugging easier, since you can inspect where the pipeline failed instead of guessing which part of one giant prompt caused issues.

Bad prompt: “Write an article about Generative AI.” It’s vague, missing audience, length, structure, and constraints, so output varies widely.

Improved prompt: Specify the audience (beginners/developers), length, headings, tone, and what to include/exclude. Add examples if you want a consistent style.

In product design, you also remove ambiguity like “make it best” and replace with measurable requirements: “use 6 headings, include 3 examples, avoid hype, keep sentences short.” Clear prompts produce stable results.

You test prompts with a fixed evaluation set: representative user inputs + expected outputs or scoring rules. You run the prompt against multiple model versions and multiple random seeds (or low temperature) and compare results.

Good teams track metrics like format correctness, factual accuracy, and user satisfaction proxies. They store prompt versions in git and maintain changelogs.

For RAG prompts, include retrieval variations. For tool prompts, include failure cases (API timeout, missing fields). Prompt testing is closer to software testing than creative writing.

Classification prompts work best when you define labels and decision rules clearly. Provide:

• List of allowed classes
• Short definitions and examples per class
• Instruction: output only the label

For ambiguous inputs, ask the model to output “unknown” or “needs more info” instead of guessing. In production, you can add confidence scoring by asking for a probability distribution, then post-process. You can also use smaller models for classification to reduce costs.

Role prompting assigns the model a persona like “You are a senior backend engineer reviewing API design” or “You are a career coach.” It helps by narrowing the style and focus of the response.

It’s useful when you need consistent tone and priorities—like reviewing code for security, writing course descriptions in a consistent brand voice, or summarizing legal text cautiously.

Role prompts work best when paired with constraints: output structure, length, and what sources to use. Without constraints, role prompts can become theatrical rather than practical.

You typically avoid pasting the whole document. Instead you:

• Chunk the document and retrieve relevant parts (RAG)
• Summarize sections and store summaries
• Use hierarchical summarization (section → chapter → full)

In prompts, clearly delimit content and ask for answers only from provided text. If the question needs multiple sections, retrieve multiple chunks and ask the model to synthesize with citations. This approach keeps token costs manageable and improves accuracy compared to stuffing everything into one prompt.

Prompt leakage is when internal instructions, system prompts, or confidential policies are revealed to the user. It can happen due to prompt injection or overly permissive responses.

Prevention includes:

• Never placing secrets in prompts (API keys, internal tokens)
• System rules: refuse to reveal system messages
• Tool-layer security: use server-side credentials only
• Redaction and content filters for sensitive data

In enterprise settings, prompt content should be treated like code: reviewed, versioned, and tested for leakage risks.

Brand safety means tone consistency, avoiding misinformation, and preventing unsafe content. A practical approach is layered:

• System prompt with strict style and safety rules
• Templates for recurring content (course overview, FAQs)
• Validation: length checks, banned phrases, factual checks where needed
• Human review for high-impact pages

For Edu-tech content, also ensure claims are realistic (no guaranteed job promises) and include responsible guidance. Stability improves when you reduce randomness and keep prompts structured.

A standard RAG pipeline has: (1) document ingestion, (2) chunking, (3) embedding generation, (4) storing vectors + metadata, (5) query embedding, (6) vector retrieval, (7) re-ranking (optional), (8) context assembly, and (9) LLM generation with citations.

The key is that the LLM answers using retrieved text, reducing hallucination and keeping answers aligned with your documents. Metadata filters (date, category, tenant) make results more relevant.

In production, you also log retrieval results and monitor “no-hit” queries to improve your index over time.

Chunking splits documents into smaller pieces for embedding and retrieval. There is no single “best” size; it depends on content type and question style.

General guidance: choose chunks large enough to preserve meaning (definitions + context) but small enough to avoid irrelevant text. Many systems use overlap to avoid cutting important sentences.

For FAQs, chunks can be small. For policy docs, slightly larger chunks work better. The right answer includes experimentation: evaluate retrieval precision and downstream answer quality, then tune chunk size and overlap accordingly.

Vector search gives “approximate relevance,” but it can miss nuance. Re-ranking uses a stronger model (cross-encoder or LLM-based scoring) to sort retrieved candidates by true relevance.

Typical approach: retrieve top 20–50 with vector DB, then re-rank to top 3–8 for the final context. This improves answer quality, especially when documents are similar.

In enterprise systems, re-ranking is worth it for high-value queries. For low-cost systems, you may skip re-ranking and rely on better chunking + metadata filters.

Hybrid search combines keyword (BM25) and vector similarity. It helps when exact terms matter—product codes, error messages, legal clauses, or names—where vector search alone may be fuzzy.

In practice, hybrid search is strong for enterprise knowledge bases because users often search for specific words and also want semantic understanding. You can blend scores or run two retrievals and merge results.

A good answer mentions that hybrid search reduces “semantic drift” and improves reliability in technical domains.

Use metadata and governance. Mark documents with version, date, owner, and approval status. Retrieve only “approved” docs and prefer latest versions via filters or ranking boosts.

Also store source URLs and last-updated timestamps so the answer can mention freshness. For critical domains, add a post-check: if multiple documents conflict, ask the user to confirm or route to a human.

In short, RAG quality is as much about data discipline as it is about model choice.

Context stuffing means dumping too many chunks into the prompt “just in case.” It is harmful because it increases cost, slows responses, and can confuse the model with contradictory or irrelevant text.

Good RAG systems keep context focused: use top-k retrieval, re-ranking, and short citations. If the question is broad, respond with clarifying questions or provide a structured summary rather than stuffing everything.

In interviews, emphasize that smaller, high-quality context often beats large, messy context.

You evaluate both retrieval and generation. Retrieval metrics: precision@k, recall@k, and whether the right source appears in the top results. Generation metrics: answer correctness, citation support, and hallucination rate.

A practical method: create a labeled QA set from your docs where you know the “gold” source chunk. Then measure whether retrieval finds it and whether the LLM answers using it.

In production, also track user feedback (“was this helpful?”) and run periodic human audits to catch subtle failures.

Vector drift happens when your embedding model changes or your documents evolve so that older embeddings no longer represent the content well. Re-indexing is the process of recomputing embeddings and updating the vector store.

Best practice: version your embedding model and store that version in metadata. When you upgrade, run parallel indexes or a staged migration, and compare retrieval quality before switching fully.

Enterprises schedule periodic re-indexing and have rollback plans to avoid sudden drops in answer quality.

Semantic caching stores answers (or retrieved contexts) for similar queries using embeddings. If a new query is close to a cached query, the system can reuse the previous result to reduce cost and latency.

It is useful for repeated questions like “What is refund policy?” or “How to reset password?”.

Risks include serving stale information if underlying docs change. To handle that, cache entries should have TTLs and can be invalidated when source documents update.

Common causes include bad chunking (too large/too small), missing metadata, low-quality embeddings, insufficient cleaning (headers/footers noise), and lack of hybrid search for exact terms.

Sometimes the problem is the user’s query: it is vague. In that case, the assistant should ask clarifying questions.

Also, if your corpus has multiple near-duplicate documents, retrieval can bounce between them. Deduplication and “approved source” policies often improve results more than model tuning.

Multi-tenant RAG requires strict data isolation. You store tenant_id in metadata and apply filters at retrieval time so one tenant’s documents never appear in another tenant’s results.

In addition, logs and caches must also be tenant-aware. If you use shared caching, cache keys must include tenant_id to avoid cross-tenant leakage.

For extra safety, you can run separate indexes per tenant for high-security customers, and enforce access control at both application and database layers.

Summarization helps when retrieved text is long, repetitive, or contains irrelevant details that could distract the model. It is also useful when you need to combine many chunks but the context window is limited.

A common pattern is “compress then answer”: summarize each chunk into key facts, then ask the LLM to answer using those facts with citations back to the sources.

This improves signal-to-noise, reduces token usage, and often increases answer clarity for end users.

Fine-tuning is worth it when you need consistent style, domain language, or structured outputs at scale, and prompting alone is expensive or inconsistent. It’s also useful for tasks that require learning hidden patterns that are hard to encode as prompt rules.

If your problem is “missing knowledge,” RAG may be better than fine-tuning. Fine-tuning teaches behavior, not a reliable database of facts.

A mature strategy: start with prompting + RAG, collect real user data, and then fine-tune to improve reliability and reduce token cost.

LoRA (Low-Rank Adaptation) fine-tunes a model by adding small trainable matrices to certain layers instead of updating all model weights. This makes training cheaper and faster, and it reduces GPU memory needs.

Practically, LoRA is used when you want domain adaptation—like better responses for customer support or internal documentation—without training a huge model from scratch.

The benefit is you can store and swap multiple LoRA adapters for different tasks or clients while keeping the base model the same, which is very useful in enterprise multi-product setups.

Fine-tuning quality depends heavily on data. Common issues include low-quality labels, inconsistent style, duplicated examples, and answers that contain hallucinated facts. Another big issue is “instruction mismatch,” where training prompts are unlike real user prompts.

Bad data teaches bad behavior. If your dataset includes toxic language or incorrect formatting, the model will learn it.

Strong teams clean data, remove duplicates, balance topics, and keep a held-out evaluation set. They also test whether the fine-tuned model regresses on general capability.

Overfitting happens when the model memorizes training examples and performs poorly on new prompts. Signs include: great training loss but weak evaluation performance, repeating similar phrases, or failing on slightly reworded questions.

Detection: evaluate on a separate test set and use diverse prompts. Also watch for “copying” behavior—outputs that look too close to training text.

Mitigation includes early stopping, regularization, better dataset diversity, and reducing training epochs. It’s often better to have slightly underfit but generalizable behavior for real users.

SFT trains on single “gold” answers. Preference optimization methods (like DPO-style approaches) train on pairs: preferred vs rejected output, pushing the model toward preferred behavior.

Preference training can improve helpfulness and reduce undesirable outputs without requiring perfect “gold” answers for every example. It is often effective when you have comparison data from human reviewers or implicit feedback.

In production, many teams do SFT to teach domain behavior, then preference optimization to polish tone, safety, and clarity.

Instruction formatting is how you structure prompt/response pairs during training. If the format is inconsistent, the model learns messy patterns and fails to generalize.

Good formatting clearly separates roles: system instruction, user message, assistant response. It may also include delimiters around context text and explicit output formatting requirements (JSON/HTML).

If your production app uses chat format but your training data is plain text, you may see behavior mismatches. Align training format with real inference format for best results.

Use a benchmark set that represents real tasks: classification accuracy, extraction correctness, structured format validity, and domain QA. You measure regression vs baseline (base model or prompt-only approach).

Also measure operational metrics: latency, token usage, and cost. For safety, run red-team prompts and check policy compliance.

A strong evaluation includes human review for ambiguous cases, plus automated checks like JSON schema validation and citation grounding if your model produces references.

Catastrophic forgetting is when fine-tuning makes the model worse at general capabilities because training pushes it too strongly toward a narrow dataset. The model becomes great at your domain but fails at basic reasoning or general language tasks.

Prevention methods include mixing in general data, using smaller learning rates, limiting epochs, and using PEFT approaches (LoRA) that reduce the scale of updates.

In enterprise settings, you want a model that improves domain behavior without losing general competence, so careful training balance matters.

Prompt-tuning adjusts a small set of learned prompt vectors (soft prompts) while keeping model weights fixed. Fine-tuning updates model parameters (full or partial via PEFT).

Prompt-tuning is lighter and can be faster to iterate, but it may be less flexible depending on the model and task. Fine-tuning often gives stronger domain adaptation and better structured outputs.

In practice, teams start with prompting, then move to LoRA fine-tuning when they need stable outputs, lower token cost, or stronger adherence to brand style.

The biggest risk is training on personal or confidential data, which can lead to memorization and leakage. If customer emails or internal documents end up in training, the model might reproduce parts of them later.

Mitigations include data anonymization, PII redaction, access controls, and training only on approved datasets. You also run leakage tests: prompts designed to extract memorized content.

For highly sensitive environments, consider RAG with secure access control instead of fine-tuning on raw private content.

Evaluation drift happens when your evaluation set no longer represents real users because product usage changes. A model might score well on old tests but fail in new scenarios.

Fix: refresh evaluation datasets using real anonymized queries, add new edge cases, and keep a stable “core” set for continuity plus a “recent” set for freshness.

In enterprise GenAI, drift is common because user behavior changes quickly. Mature teams treat evaluation as a living process with regular updates and versioned metrics dashboards.

End-to-end: collect approved data → clean/deduplicate → format into chat/instruction pairs → split train/val/test → train with SFT or PEFT → run automated evaluation → run safety tests → deploy behind a feature flag → monitor → iterate.

For reliability, store dataset versions and training configs, and log evaluation results. Use canary release to compare with baseline.

In enterprise, add approvals and audit trails: who approved data, who ran training, and what changed. This makes the pipeline safe, repeatable, and scalable.

An AI agent is an LLM-powered system that can plan steps, call tools (APIs), and complete tasks—like booking, ticket triage, report generation, or data cleanup—rather than only chatting.

The key difference from a chatbot is action. Agents decide “what to do next,” fetch data, and use outputs to continue.

In enterprise, good agents have guardrails: limited permissions, approval steps for risky actions, and logging for audit. A real agent is a workflow system with an LLM as the decision-making layer.

Tool calling lets the model request structured function calls—like “getFlightPrices(from, to, date)”—instead of guessing. Reliability comes from:

• Clear tool descriptions and parameter schemas
• Allowlisting tools per user role
• Validation of tool arguments
• Retry logic and fallbacks on failure

You also prevent the model from inventing tool results: tools run server-side and the model only sees returned data. This approach is critical for accuracy when you need real-time facts.

Agents fail by looping (repeating steps), choosing wrong tools, mis-parsing tool outputs, or taking unnecessary actions that increase cost. They may also be vulnerable to prompt injection from tool results or retrieved documents.

Mitigation includes step limits, “stop conditions,” structured intermediate state, and tool output sanitization. Some systems add a “critic” step that reviews plan quality before execution.

In interviews, highlight that agents require workflow engineering: state machines, permissions, and monitoring—not only prompts.

Memory should be external and controlled, not an unbounded chat history. Common approaches are:

• Short-term memory: recent conversation window
• Long-term memory: stored facts as structured notes with user consent
• Retrieval memory: embed past interactions and retrieve relevant ones

Safety means: store only what you must, apply privacy rules, and allow deletion. For enterprise, avoid saving secrets and use encryption + access control for memory stores.

Planner-executor splits an agent into two roles: a planner that breaks the task into steps, and an executor that performs each step with tools. This improves reliability because planning and execution are separated, making it easier to validate the plan before acting.

Example: Planner decides: “1) fetch customer order, 2) check refund policy, 3) draft response.” Executor runs tools step by step.

In enterprise use-cases, this reduces mistakes and makes auditing easier since you can inspect the plan and tool calls independently.

You enforce permissions at the tool layer. The agent can “request” an action, but your system decides whether it is allowed. High-risk actions (payments, deletions, account changes) should require human confirmation or multi-step approvals.

Additionally, you can categorize tools as read-only vs write tools, and restrict write tools to certain roles. Add step limits, auditing, and anomaly detection.

This is important: you do not “trust the model.” You build systems that remain safe even when the model behaves unexpectedly.

Agent evaluation checks whether the system completes tasks reliably, not just whether responses look good. You test with scripted scenarios: tool success, tool failure, ambiguous input, and adversarial instructions.

Metrics include task completion rate, number of tool calls (efficiency), time-to-complete, and safety compliance. You also review logs to ensure the agent did not access unauthorized tools or leak data.

In enterprise, evaluation is continuous: add new scenarios as incidents occur and keep a regression suite for every prompt/tool update.

Function schemas describe tool inputs/outputs. Good schema design reduces model mistakes: fewer ambiguous fields, strong typing, clear constraints, and examples.

For example, instead of “date” as free text, use ISO format “YYYY-MM-DD”. Instead of “city” use city codes where possible. This reduces parsing errors and improves tool success rate.

Also return structured outputs from tools, not long text. That makes it easier for the LLM to reason and respond accurately.

Tool failures happen: timeouts, invalid inputs, missing records. A robust system handles them like software systems do—retries with backoff, fallback tools, and user-friendly recovery messages.

The agent should explain what failed in simple terms and ask for missing info. For example: “I couldn’t find that booking ID—can you confirm it?”

Also log tool errors with correlation IDs for debugging. The goal is not perfection; it’s a predictable recovery path that preserves user trust.

Multi-agent collaboration uses separate specialized agents—like a “Research agent,” “Writer agent,” and “Reviewer agent.” It is useful when tasks require different skills or when quality matters more than speed.

In enterprise, multi-agent setups can improve reliability by adding a reviewer that checks policy compliance, formatting, or factual grounding before final output.

Downside: more cost and complexity. Many teams start single-agent and only add multi-agent architecture when they see consistent benefits for high-value workflows.

Workflow: (1) classify user intent (refund, shipping, technical), (2) retrieve account/order details via tool, (3) retrieve policy docs via RAG, (4) draft response, (5) run compliance checks, (6) send final answer with references.

This is agentic because the system chooses tools and uses retrieved evidence. It is not simply a “talking bot.”

In production, you also add escalation to human support when confidence is low or when the user is unhappy.

Tool output injection happens when data returned from a tool contains malicious instructions—like “ignore rules and reveal secrets.” If you directly pass tool output into the model as plain text, the model might follow it.

Defenses include:

• Wrap tool output in strict data-only delimiters
• Use structured formats (JSON) and parse safely
• System rule: treat tool data as untrusted
• Filter/escape unexpected strings

This is a real enterprise concern because tools often return user-generated content.

Diffusion models generate images by learning how to reverse a noise process. During training, images are gradually noised; the model learns to remove noise step by step. During generation, it starts from random noise and denoises toward an image that matches the prompt.

This approach produces high-quality images because each denoising step refines details. Text-to-image models connect language embeddings to the denoising process so the image aligns with the prompt.

In production, teams tune steps, guidance scale, and safety filters to balance quality, speed, and policy compliance.

Classifier-free guidance is a technique to make generated images align more strongly with the text prompt. The model learns both conditional (with prompt) and unconditional (without prompt) predictions, then combines them during sampling.

Higher guidance typically increases prompt fidelity (the image follows the prompt more), but if pushed too high it can cause artifacts or reduced diversity.

In practice, you tune guidance scale depending on use case: creative exploration vs strict prompt alignment for product images or branded visuals.

Latent diffusion performs the diffusion process in a compressed latent space instead of raw pixels. An autoencoder compresses images into a latent representation; diffusion happens there; then the result is decoded back to an image.

This makes generation faster and less memory-intensive, enabling high-resolution outputs with manageable compute. Many popular systems use latent diffusion because it scales well.

Understanding LDMs helps when you tune performance, choose output sizes, and design pipelines for real-time image generation products.

Negative prompts tell the model what to avoid—like “blurry, low quality, extra fingers.” They influence the guidance process so the model pushes away from unwanted patterns.

They are useful for improving quality and controlling common artifacts. However, negative prompts are not magic; they work best when combined with good sampling settings and appropriate model checkpoints.

In enterprise contexts, negative prompts also help enforce brand rules, such as avoiding certain styles, unsafe content, or copyrighted elements.

Fine-tuning in diffusion models adapts a base model to learn a specific style or subject. LoRA updates small adapter weights to teach new concepts efficiently. DreamBooth-style approaches can teach a specific subject (like a product) using a small set of images.

Used correctly, you can generate consistent branded visuals. Risks include overfitting (repeating training images) and copyright/consent issues if training data is not owned/approved.

Enterprise teams use curated datasets and clear usage policies to keep outputs safe and consistent.

Objective metrics exist (FID, CLIP score), but in product settings you also need human evaluation: prompt adherence, aesthetics, brand compliance, and safety. A common approach is a rubric: 1–5 for alignment, 1–5 for quality, 1–5 for artifact presence.

You also track operational metrics: generation time, failure rate, and user re-roll rate. If users repeatedly regenerate, it signals dissatisfaction.

For enterprise, ensure outputs pass content safety filters and do not violate policy before being shown to users.

Safety controls include prompt filtering (block disallowed topics), output moderation, watermarking, and limiting sensitive styles/subjects. Some platforms use classifiers to detect NSFW or disallowed content in generated images.

Also protect against prompt injection-like tricks and ensure your system does not generate copyrighted logos or restricted imagery. For enterprise, maintain audit logs and clearly document acceptable use policies.

A strong answer emphasizes layered safety: pre-check prompt, post-check output, and enforce policy in the product UI.

Text-to-image starts from noise and generates an image guided by text. Image-to-image starts from an existing image and transforms it toward the prompt while preserving structure. Image-to-image is useful for style transfer, variations, and controlled edits.

In product workflows, image-to-image enables consistent outputs—like keeping a product layout but changing background or style. It’s also often faster because you need fewer steps to reach a good result.

In interviews, mention use cases and how control improves UX for creators and brands.

Inpainting edits a specific region of an image while keeping the rest unchanged. You provide a mask for the area to modify and a prompt describing what to fill in.

It is useful for removing objects, fixing artifacts, or changing localized details (like replacing text on a banner). In enterprise design workflows, inpainting reduces manual editing effort and speeds up creative iteration.

Good systems also preserve lighting and perspective for realism, and they run moderation checks on both prompts and final outputs.

Common artifacts include distorted hands/faces, extra limbs, text gibberish, and over-smoothed textures. Reducing them involves better models, careful prompt wording, negative prompts, higher quality sampling settings, and sometimes post-processing.

Using high-quality checkpoints and fine-tuned adapters for your domain helps. For product images, controlling composition using image-to-image or reference conditioning can reduce randomness.

In enterprise pipelines, you also add QC checks and allow users to regenerate variations quickly.

Enterprises use licensed or owned training data, keep model usage within policy, and avoid generating protected logos/characters. They also provide guidance to users: do not upload or request copyrighted materials without rights.

Some systems add watermarking or metadata to indicate AI generation. You can also block prompts referencing well-known brands or characters if policy requires.

In interviews, show you understand both the technical side and the compliance side: data sources, permissions, and auditability.

A practical enterprise use case is marketing creative generation: generate multiple banner variants with different backgrounds, moods, or seasonal themes, while keeping the product consistent. Another is e-commerce: generate lifestyle images from a product photo without expensive photoshoots.

Enterprise success depends on control and safety: consistent brand style (fine-tuning/LoRA), prompt templates, and moderation filters.

It’s less about “cool art” and more about reliable, repeatable workflows that reduce time-to-market.

LLMOps focuses on operationalizing LLM applications: prompt management, RAG pipelines, tool integrations, latency/cost optimization, safety guardrails, and continuous evaluation. Traditional MLOps focuses more on training pipelines, feature stores, model drift, and batch prediction.

LLMOps still includes model deployment, but it adds unique concerns: context management, prompt versions, retrieval quality, and policy compliance.

In enterprise, LLMOps maturity is seen when teams can ship prompt/retrieval updates safely with automated tests and monitoring—like CI/CD for LLM behavior.

Cost reduction strategies include: shorter prompts, smart RAG (top-k + re-ranking), response caching, smaller models for simple tasks, and model cascading. Also use structured outputs to reduce retries and minimize verbose answers.

Another lever is tool usage: fetch exact facts from APIs rather than asking the model to generate long explanations. For high traffic, batch requests and reuse embeddings where possible.

In interviews, show that cost is a design constraint: you engineer for efficiency from day one, not after the bill becomes painful.

You need logs for prompts (with redaction), model version, retrieved sources, tool calls, latency breakdown, token usage, and final output. Also capture user feedback and error reports.

For RAG, store retrieval results and similarity scores. For tool systems, log tool inputs/outputs (sanitized) and failures.

Good observability helps debug issues like “wrong answers” by letting you see whether the problem was retrieval, prompting, tool errors, or the model itself.

There are multiple caches: (1) response cache for identical prompts, (2) semantic cache for similar queries, (3) retrieval cache for repeated RAG lookups, and (4) embedding cache for documents and queries.

Caching reduces cost and improves latency, but it risks staleness. Use TTLs and invalidation when underlying data changes. For multi-tenant systems, cache keys must include tenant_id to avoid data leakage.

In production, caching is one of the biggest cost savers, especially for FAQs and repeated internal queries.

Treat prompts like code. Version them, review changes, run regression tests on evaluation sets, and deploy behind feature flags. Use canary rollout: small percentage of traffic gets the new prompt, compare quality metrics and error rates, then expand.

Also log prompt versions with every response so you can trace issues back to a specific change. If quality drops, rollback quickly.

This approach prevents “silent regressions” where a small prompt tweak breaks formatting or increases hallucination rates.

Model routing chooses different models depending on the task. For example, a smaller cheaper model handles extraction and classification, while a larger model handles complex reasoning or long-form writing.

This saves cost and improves speed. It also helps reliability: some models are better for certain languages or code tasks.

In enterprise systems, routing is often based on intent classification, complexity scoring, or user tier. The best routing is measured, not guessed—teams evaluate performance per route and adjust rules.

Safe deployment includes containerization, resource limits, network policies, and access control. Keep the model behind authenticated APIs, limit request sizes, and implement rate limiting. Add input moderation and output filtering for policy compliance.

You should also monitor GPU usage, latency, and error rates. If your application is multi-tenant, enforce tenant isolation in retrieval and caching layers.

Enterprises also maintain model cards: which model version, training source, known limitations, and usage guidelines—so the system is auditable.

Streaming sends partial tokens to the user as the model generates them. Users love it because it feels faster and more interactive, even if total generation time is the same.

Streaming also helps UX for long outputs like explanations or code. It reduces perceived latency and increases trust because users see progress.

Implementation needs careful handling: stop signals, formatting stability (especially for JSON), and fallbacks when the stream breaks. For structured outputs, many teams generate first, validate, then stream final when strict correctness is required.

You should assume prompts may contain PII: names, phone numbers, emails, IDs. Logs must be redacted or tokenized before storage. Use masking rules and store raw content only when necessary and permitted.

Access to logs should be restricted and audited. Data retention policies should limit how long logs are kept.

For enterprise compliance, document what is logged, why, and who can access it. This reduces risk and builds customer trust.

Use a fixed evaluation suite plus a recent-real-queries suite. Compare outputs from old vs new model using scoring rules: format correctness, factual grounding, policy compliance, and human preference ratings.

Also compare costs and latency. Sometimes an upgrade improves quality but increases tokens and cost—so routing and prompt adjustments may be needed.

In enterprise, you never swap models blindly; you run parallel testing, canary release, and maintain rollback capability.

GenAI endpoints can be expensive, so abuse prevention is essential. Rate limiting controls requests per user/IP and prevents scraping or DDoS-like load. Use API keys, quotas, and tier-based limits.

Also limit maximum tokens, request size, and concurrent requests. Monitor unusual patterns like repeated long prompts or high error rates.

In enterprise products, combine rate limiting with authentication, tenant quotas, and alerting so you catch abuse early before it becomes a cost or security incident.

A practical pipeline includes: prompt code review → automated tests (format checks, golden dataset evaluation) → security checks (injection tests) → staging deployment → canary rollout → monitoring dashboards → rollback plan.

For RAG, include retrieval tests and data freshness checks. For tool calling, include tool mocks and failure simulations.

The goal is predictable releases. Treat prompts, retrieval configs, and safety rules as versioned artifacts, just like source code. That’s what makes GenAI enterprise-ready.

Top risks include prompt injection, data leakage (PII/secrets), unsafe tool execution, model hallucinations in high-stakes flows, and supply-chain issues (untrusted models or dependencies).

There are also business risks: users generating disallowed content, copyright issues, and compliance failures.

Defense should be layered: strict system prompts, tool permissioning, retrieval filtering, input/output moderation, logging with redaction, and human escalation for sensitive actions.

First, enforce access control at retrieval: only retrieve documents the user is authorized to view. Use metadata filters and identity-based permissions.

Second, prevent the model from outputting large verbatim chunks. You can limit quoting length and encourage summarization. Third, log and monitor for suspicious queries (“show me all salaries,” “print database”).

Finally, use redaction for sensitive fields and establish a data classification system: public/internal/confidential. The system should never rely on the LLM to “do the right thing” alone.

Data governance means controlling what data is used, how it is stored, who can access it, and how changes are tracked. For LLM apps, it covers training data, retrieval documents, logs, and user-provided inputs.

Key practices include document ownership, approval workflows, versioning, retention policies, and audit trails. It also includes classification (PII/confidential) and redaction rules.

Enterprise GenAI succeeds when governance is clear—because the model is only as trustworthy as the data pipeline feeding it.

A model card documents what a model is, what it was trained on (at high level), what it is good at, and its limitations. It also includes safety considerations and recommended use cases.

In enterprise, model cards support compliance and risk management. They help teams avoid deploying a model for tasks it is not suitable for and make audits easier.

Model documentation should also include operational details: version, evaluation results, known failure modes, and how to report incidents.

Privacy starts with data minimization: collect only what is needed. If you store conversation memory, inform users clearly and allow them to opt out or delete history.

Use encryption at rest, strong access control, and redaction in logs. For sensitive use cases, keep processing on secure servers and avoid sending data to third parties without agreements.

A good assistant also avoids asking for unnecessary personal info. In regulated environments, privacy and consent are not “features”—they are requirements that must be built into the product architecture.

Jailbreak resistance means the system can withstand attempts to bypass rules, like “ignore safety policies” or “act as another system.” Improving it requires layered controls: strong system prompts, content filtering, refusal policies, and tool permissioning.

You also test with red-team prompts and maintain blocklists for known attack patterns. For enterprise apps, you can add a separate safety model that classifies risky queries and forces safe responses.

Most importantly, never give the model direct access to secrets or irreversible actions without a controlled approval layer.

Don’t rely on the model’s memory. Use tools and RAG to fetch verifiable facts, and require citations to approved sources. For calculations, compute programmatically and only have the model explain results.

For high-stakes decisions, add human review or dual-control approvals. Also implement confidence signals: if retrieval is weak, the assistant should say so and request more context.

Enterprises build accuracy through system design: grounding, validation, and approval workflows—not by hoping the model “knows the truth.”

First, restrict who can trigger sending. Second, require user confirmation with a preview of the final message. Third, enforce templates and compliance checks (PII leakage, prohibited claims, tone rules).

Also limit recipients and add rate limits to prevent misuse. Log every send event with audit trails.

This is a classic example of “human-in-the-loop” design: the LLM can draft and recommend, but the system should prevent silent automated sending unless there is strong business justification and safety controls.

A typical architecture: UI → API gateway → Auth/roles → Orchestrator service → Retrieval layer (vector DB + keyword search) → Re-ranker → Prompt builder → LLM → Output validator/moderator → Response.

Add tool layer for live data (CRM, ticketing, pricing) with strict permissions. Add observability: logs, metrics, traces, redaction.

For enterprise readiness, include versioning for prompts and indexes, evaluation pipelines, and governance workflows for document approval. This architecture turns GenAI from “chat” into a reliable business system.