50+ GenAI Interview Questions: From Transformers to RAG With Expert Answers

The most common gen ai interview questions cover seven areas: transformer and attention mechanics, RAG versus fine-tuning, chunking and embedding selection, hallucination mitigation, evaluation methodology, agent and tool design, and cost or latency optimisation. RAG and evaluation together make up the largest share of a modern interview loop.

Below are 60 questions with answers written the way a strong candidate would actually say them, plus a note on what each question is really testing. Definitions get you past the screen. Trade-offs get you the offer.

Straight Answers to the Questions You Are Here For

What are the most common GenAI interview questions? Transformer and attention mechanics, RAG versus fine-tuning, chunking and embedding choices, hallucination mitigation, evaluation methodology, agent design, and cost or latency optimisation. Roughly 60% of a modern loop is RAG, evaluation and production judgement.

What is the difference between RAG and fine-tuning? RAG injects external knowledge into the prompt at inference time without changing model weights. Fine-tuning changes the weights through additional training. RAG is for knowledge, fine-tuning is for behaviour. Most production systems use both.

How do you reduce hallucinations? Ground answers in retrieved sources, instruct the model to answer only from provided context and say it does not know otherwise, lower temperature for factual tasks, enforce citations, and add a verification or confidence check. You reduce hallucinations, you do not eliminate them.

How do transformers work in one sentence? A transformer processes all tokens in a sequence in parallel and uses self-attention to weigh how much every token should influence every other token, which is what replaced the sequential bottleneck of RNNs.

What is the hardest round? The system design round, consistently. It shifted between 2024 and 2026 to centre on RAG architecture, evaluation methodology and agentic design, and most prep material has not caught up.

How many questions should you prepare? Roughly 8 to 10 per category across LLM fundamentals, RAG, prompting, agents, evaluation and system design. That is 50 to 60 total, with depth concentrated on RAG and evaluation.

Do you need a degree for a GenAI role in India? No, but you need demonstrable production judgement. A portfolio with an evaluation harness beats a certificate. A degree helps mainly for visa routes, GCC leadership tracks and roles that filter on credentials.

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Key Takeaways

• Definitions get you past the screen. Trade-offs get you the offer. Every answer below pairs the concept with a decision.
• RAG and evaluation dominate. If you have limited prep time, spend it here rather than on transformer internals.
• Say the number. Chunk sizes you used, latency you hit, cost per conversation. Vague answers read as second-hand knowledge.
• Volunteer failure modes. Strong candidates name how the system breaks before the interviewer asks.
• One evaluation story is worth ten framework names. How you knew a change did not make things worse is the single highest-signal thing you can say.

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How GenAI Interview Rounds Are Structured

Most GenAI loops in 2026 have five recognisable stages, whatever the internal naming.

Round	Typical prompt	What separates a strong answer
Recruiter or hiring manager screen	“Walk me through a GenAI feature you shipped.”	Names a metric moved and a failure caught in evaluation, not just the stack
Practical coding, about an hour	Chunk a document, implement cosine similarity, wrap a flaky model API with retries	Treats rate limits, partial failures and token cost as first-class
Retrieval and LLM design	“Design an assistant over 2 million support articles, 2 second budget”	Reaches for the boring measurable option first, defers scale until numbers force it
Evaluation	“How do you know a prompt change did not make things worse?”	Golden set, RAG triad, regression run before ship
Behavioural	“Tell me about a model that misbehaved in production.”	Monitoring, rollback, and what the evaluation gap turned out to be

Startups frequently add a take-home: build a small RAG service or agent over a corpus they provide, then defend the choices live. Expectation scales with seniority. Mid-level candidates need a reasonable architecture. Senior and staff candidates are expected to reason across uncertainty and defend cost against quality with numbers.

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Section 1: LLM and Transformer Foundations (Q1 to Q12)

Q1. Explain the transformer architecture and self-attention. A transformer encodes a sequence into token embeddings plus positional information, then applies stacked self-attention and feed-forward layers. Self-attention computes query, key and value projections for every token, scores each token against every other, and produces a weighted sum. The result is that each token’s representation is contextualised by the whole sequence, computed in parallel rather than step by step. Testing: whether you understand the mechanism or just the vocabulary. This deep learning tutorial and large language models guide cover the underlying architecture if this is shaky.

Q2. Why did transformers replace RNNs and LSTMs? Two reasons. Parallelism: RNNs process tokens sequentially, so training cannot be parallelised across the sequence, while transformers process the whole sequence at once. Long-range dependencies: RNNs degrade over distance even with gating, whereas attention gives any token direct access to any other token in one hop.

Q3. What is the difference between encoder-only, decoder-only and encoder-decoder models? Encoder-only such as BERT sees the full sequence bidirectionally and is suited to classification and embedding. Decoder-only such as GPT is causally masked and predicts the next token, which suits generation. Encoder-decoder such as T5 encodes an input then decodes an output, which suits translation and summarisation. Almost all current chat LLMs are decoder-only.

Q4. What is tokenization and why does it matter practically? Tokenization splits text into subword units the model actually consumes. BPE, WordPiece and SentencePiece are the common algorithms. It matters because cost and context limits are billed in tokens, not words, and because Indian languages and code often tokenise inefficiently, inflating cost and consuming context faster than an English word count suggests.

Q5. What are embeddings? Dense vector representations where semantic similarity corresponds to geometric proximity. They power retrieval, clustering, deduplication and classification. In interviews, the follow-up is always how you choose one, so be ready with dimensionality, domain fit, cost, and whether you benchmarked it on your own data. This NLP tutorial covers the representation fundamentals.

Q6. What is a context window, and what happens when you exceed it? The maximum tokens a model can attend to in one call, including system prompt, history, retrieved context and output. Exceeding it causes truncation or an error. Mitigations: conversation summarisation, sliding windows, retrieval instead of stuffing, and pruning history by relevance rather than recency alone.

Q7. Explain temperature, top-k and top-p. Temperature scales the logits before sampling, so lower values concentrate probability on likely tokens. Top-k samples from the k most likely tokens. Top-p, or nucleus sampling, samples from the smallest set whose cumulative probability exceeds p, which adapts to the shape of the distribution. Use low temperature for factual and code tasks, higher for ideation. Tune one, not all three at once.

Q8. What is the KV cache and why does it matter? During generation the model caches key and value tensors for previously generated tokens so it does not recompute attention over the full prefix at each step. It converts quadratic recomputation into incremental work, which is the main reason streaming generation is affordable. It also consumes GPU memory proportional to sequence length and batch size, which is often the real serving bottleneck.

Q9. What is the difference between pre-training, fine-tuning and RLHF? Pre-training builds general capability from a large corpus via next-token prediction. Fine-tuning specialises the model on curated task data. RLHF aligns behaviour to human preferences using a reward model trained on comparisons. In short: capability, specialisation, alignment.

Q10. Generative versus discriminative models, in one line each? A discriminative model learns the boundary between classes and estimates P(y|x). A generative model learns the data distribution and can sample new instances, estimating P(x) or P(x|y).

Q11. Compare GANs, VAEs and diffusion models. GANs train a generator against a discriminator, produce sharp samples, and are unstable to train with mode collapse risk. VAEs learn a probabilistic latent space, train stably, and tend to produce blurrier output. Diffusion models iteratively denoise from noise, currently dominate image generation on quality and controllability, and are slower at inference. Testing: breadth beyond text, common when the role touches multimodal work.

Q12. What are the main limitations of current LLMs? Hallucination, a fixed knowledge cutoff, bounded context, no reliable internal notion of confidence, sensitivity to prompt phrasing, weak arithmetic and multi-hop reasoning without scaffolding, and non-determinism that makes testing harder than in conventional software. Naming non-determinism specifically signals production experience.

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Section 2: Prompt Engineering (Q13 to Q19)

Q13. Zero-shot, one-shot and few-shot: when do you use each? Zero-shot when the task is common and the instruction is unambiguous. Few-shot when output format matters or the task is idiosyncratic, since examples specify structure more reliably than description. The trade-off is token cost per call, which matters at volume.

Q14. What is chain-of-thought prompting and when should you avoid it? Prompting the model to reason step by step before answering, which improves multi-step arithmetic and logic. Avoid it for simple classification where it adds latency and cost without accuracy gain, and be aware that reasoning-tuned models already do this internally, so explicit CoT can be redundant or even harmful.

Q15. Explain ReAct prompting. Interleaving reasoning traces with actions, where the model thinks, calls a tool, observes the result, and repeats. It is the pattern underneath most tool-calling agent loops and the direct ancestor of modern agent frameworks.

Q16. How do you design a production system prompt? Define the role, scope and refusal boundaries. Specify output format explicitly, ideally with a schema. State what to do under uncertainty. Keep it version controlled and treat changes as deploys with an evaluation gate. The signal here is treating prompts as code rather than as a text box.

Q17. What is structured output and function calling? Constraining the model to emit output conforming to a schema, typically JSON, which makes responses machine-consumable. Function calling extends this by letting the model select a tool and produce validated arguments. Always validate the output anyway, because schema adherence is high but not guaranteed.

Q18. How do you handle prompt injection? Treat all retrieved and user content as untrusted. Separate instructions from data, restrict tool permissions to least privilege, require human approval for irreversible actions, sanitise and filter retrieved chunks, and add output checks. There is no prompt wording that reliably solves this, and saying so is the correct answer.

Q19. How do you show real skill in prompt engineering rather than claiming it? Show a before-and-after with a measured failure rate on a held-out set. “I reduced malformed JSON from 8% to under 1% by adding a schema and two examples” is worth more than any description of your prompting philosophy. Guide to prompt engineering covers the systematic version of this.

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Section 3: RAG Architecture (Q20 to Q32)

This is the highest-yield section. If your preparation time is limited, spend it here.

Q20. Explain RAG end to end. Ingest and chunk documents, embed the chunks, store them in a vector index. At query time, embed the query, retrieve the top candidates by similarity, optionally rerank, assemble the context with the prompt, generate a grounded answer, and attach citations. It addresses hallucination, knowledge cutoff and access to private data without retraining. This  RAG guide is a good refresher.

Q21. RAG versus fine-tuning: how do you choose? Default to prompting. Add RAG when the gap is knowledge, especially when it changes frequently, is too large to bake into weights, or must be cited. Fine-tune when the gap is behaviour, meaning consistent format, tone or domain reasoning that prompting and retrieval cannot fix. In production they combine: fine-tune for form, retrieve for facts. Testing: this is the single most frequently asked GenAI question. Answer with the decision framework, not the definitions.

Q22. How do you choose a chunking strategy? Start with recursive character splitting around 500 to 1,000 tokens with 10 to 20% overlap, then adapt to structure. Chunk along semantic boundaries where the document has them: sections in documentation, functions in code, rows in tables. Too small loses context, too large dilutes the embedding and wastes context window. The correct answer includes that you evaluated it rather than picked it.

Q23. How do you choose an embedding model? Benchmark on your own retrieval set rather than trusting a leaderboard. Consider domain fit, dimensionality against index cost, multilingual need, maximum sequence length, and whether it can be self-hosted for data residency. Changing the embedding model means reindexing everything, so the switching cost is real.

Q24. What is hybrid search and why is it better than pure vector search? Combining dense vector similarity with sparse keyword matching such as BM25, then fusing the rankings. Dense retrieval handles paraphrase and semantics but misses exact identifiers, error codes, part numbers and rare terms. Keyword search catches exactly those. Hybrid retrieval is close to the default in serious systems for this reason.

Q25. What is reranking and when is it worth the latency? A cross-encoder scores each retrieved candidate against the query jointly rather than by vector distance, producing much better ordering. You retrieve a wide set, say 50, then rerank down to 5. Worth it when precision matters more than the added 100 to 300 milliseconds, which is most enterprise use cases.

Q26. How do you evaluate a RAG system? Split retrieval from generation. Retrieval: precision@k, recall@k, MRR, NDCG against a labelled set. Generation: faithfulness, meaning is the answer supported by retrieved context, and answer relevance. The RAG triad of context relevance, groundedness and answer relevance is a compact framing interviewers recognise. Build a golden set of 100 to 200 real questions with expected answers before you optimise anything.

Q27. Your RAG system gives wrong answers. How do you debug it? Bisect the pipeline. Check whether the correct chunk was retrieved at all. If not, the failure is retrieval: chunking, embeddings, or query phrasing. If it was retrieved but the answer is still wrong, the failure is generation: prompt, context ordering, or model capability. This bisection is the answer interviewers want, because most candidates start guessing at prompts.

Q28. What is the lost in the middle problem? Models attend more reliably to content at the beginning and end of a long context than to the middle. Mitigate by retrieving fewer, higher-quality chunks, reranking so the best material sits at the edges, and resisting the temptation to stuff the full context window just because it is available.

Q29. What is query transformation? Rewriting the user query before retrieval. HyDE generates a hypothetical answer and embeds that instead, since answers embed closer to answers than questions do. Query decomposition splits multi-hop questions into sub-queries. Step-back prompting generalises the question to retrieve broader context first.

Q30. How do you handle multi-hop questions? Decompose into sub-questions, retrieve for each, then synthesise. Or use an agentic loop that retrieves, inspects, and decides whether it needs more. Single-shot retrieval fundamentally cannot answer questions requiring a chain of facts across documents.

Q31. How do you keep a RAG index fresh? Incremental updates keyed on document identifiers with versioning, deletion handling so removed documents leave the index, and change detection through hashing rather than full reindexing. Serving stale answers confidently is a common production failure, and mentioning deletion specifically signals you have run one of these.

Q32. How do you scale RAG to millions of documents under a latency budget? Approximate nearest neighbour indexing such as HNSW or IVF with tuned recall, metadata filtering to narrow the candidate space before vector search, caching for repeated and near-duplicate queries, and reranking only a small candidate set. Start with pgvector on existing Postgres and move to a dedicated vector store when measurements justify it, not before. Testing: whether you over-engineer. Reaching straight for a managed vector database at 500 documents is a negative signal.

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Section 4: Fine-Tuning and Model Adaptation (Q33 to Q40)

Q33. What is LoRA and why does it work? Low-Rank Adaptation freezes base weights and trains small low-rank matrices injected into the layers, exploiting the observation that adaptation updates have low intrinsic rank. It cuts trainable parameters by orders of magnitude, so fine-tuning fits on modest hardware, and adapters can be swapped per task at serving time (original paper).

Q34. What is QLoRA? LoRA applied on top of a base model quantised to 4-bit, with techniques to preserve quality. It brings fine-tuning of large models within reach of a single consumer or mid-range cloud GPU, at some cost in throughput.

Q35. What is PEFT more broadly? The family of parameter-efficient fine-tuning methods including LoRA, adapters, prefix tuning and prompt tuning. All share the goal of adapting behaviour without updating the full parameter set.This tutorial of  machine learning covers the training mechanics underneath these methods.

Q36. How do you prepare a fine-tuning dataset? Quality beats volume decisively. A few thousand clean, consistent, correctly formatted examples usually outperform tens of thousands of noisy ones. Deduplicate, hold out a genuine test split, keep formatting identical to inference-time formatting, and audit for label noise. Most failed fine-tunes are data problems, not hyperparameter problems.

Q37. What is instruction tuning? Fine-tuning on instruction and response pairs so the model follows directions rather than merely continuing text. It is what converts a raw pre-trained model into something usable in a chat interface.

Q38. Which hyperparameters matter for fine-tuning? Learning rate first, and it should be much lower than pre-training. Then epochs, where two to three is often enough and more invites overfitting or catastrophic forgetting. Then LoRA rank and alpha, batch size, and warmup. Validate on held-out data every epoch, not just at the end.

Q39. What is catastrophic forgetting and how do you avoid it? The model loses general capability while specialising. Mitigate with lower learning rates, fewer epochs, PEFT rather than full fine-tuning since base weights stay frozen, and mixing in general-purpose data. Always evaluate on general benchmarks after fine-tuning, not only on your task.

Q40. What is quantization and what does it cost you? Reducing weight and activation precision, typically to INT8 or INT4, cutting memory and increasing throughput. Quality degradation is usually modest at INT8 and more variable at INT4, hitting reasoning-heavy and long-context tasks hardest. The honest answer names the trade-off rather than treating it as free.

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Section 5: Agents and Orchestration (Q41 to Q46)

Q41. What is an AI agent, and how does it differ from a chain? A chain is a predetermined sequence of steps. An agent decides its own control flow at runtime, choosing which tools to call and when to stop. The distinction that matters in production is that agents are non-deterministic in structure as well as output, which makes them harder to test, budget and debug. This guide to agentic AI goes deeper.

Q42. How do you design tools for an agent? Clear names, precise descriptions since the model selects on them, narrow typed schemas, and least-privilege scoping. Make destructive operations idempotent or approval-gated. Return structured errors the model can recover from rather than raw stack traces. Too many overlapping tools degrades selection accuracy noticeably.

Q43. What is LangGraph and when would you use it over LangChain? Since the v1.0 releases in late 2025 they are layers rather than rivals: LangChain’s create_agent runs on the LangGraph runtime. Use LangChain for a standard tool-calling loop, and drop to LangGraph’s explicit graph when you need custom cycles, durable checkpointed state, human approval gates or multi-agent handoffs (LangChain docs). Testing: whether your knowledge is current. Describing them as competing frameworks dates you to before October 2025.

Q44. How do you stop an agent looping forever? Recursion and step limits, a token budget per run, a wall-clock timeout, cost caps, and a terminal condition that does not rely solely on the model deciding to stop. Log every loop that hits a limit, because that is your signal that the task design is wrong.

Q45. How do you design agent memory? Separate short-term working state within a run from long-term memory across runs. Short-term is the message history with summarisation as it grows. Long-term is usually retrieval over stored facts with explicit write policies, since writing everything degrades retrieval quality. The hard part is deciding what to forget, and saying that shows you have built one.

Q46. Single-agent or multi-agent? Default to single-agent with good tools. Multi-agent is justified when subtasks need genuinely different context, tool permissions or models, and when the coordination overhead is less than the benefit. Multi-agent systems multiply token cost and failure surface, so the burden of proof sits on the multi-agent design.

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Section 6: Evaluation, Cost and Production (Q47 to Q55)

This section is where most candidates are thin, which makes it the fastest place to differentiate.

Q47. How do you evaluate LLM output quality beyond looks good? A golden dataset of real inputs with expected behaviour, automated metrics appropriate to the task, LLM-as-judge with a rubric for open-ended output, and human review on a sample for high-stakes cases. Run it as a regression suite gating deploys. Reference metrics such as BLEU and ROUGE are weak proxies for open-ended generation and you should say so.

Q48. What is LLM-as-a-judge and what are its weaknesses? Using a strong model to score outputs against a rubric. It scales cheaply and correlates reasonably with human judgement when the rubric is specific. Weaknesses: position bias, verbosity bias, self-preference toward its own outputs, and drift when the judge model is updated. Calibrate against human labels periodically.

Q49. How do you know a prompt change did not make things worse? Run the golden set before and after, compare per-category results rather than a single average, and check the tail rather than the mean. This is the most predictive question in the whole loop. If the answer is “I tested a few examples manually,” the interview is effectively over.

Q50. What is the difference between offline and online evaluation? Offline runs against a fixed dataset before deployment and catches regressions. Online measures real traffic through A/B tests, user feedback signals, deflection or resolution rates, and escalation counts. Offline evaluation that looks great while online metrics stay flat usually means your golden set does not reflect real queries.

Q51. How do you manage LLM cost in production? Model routing, sending easy requests to a small cheap model and hard ones to a large model. Semantic caching for near-duplicate queries. Prompt compression and context pruning. Batch APIs for asynchronous work. Retrieving fewer, better chunks rather than filling the window. Then measure cost per successful task, not cost per call, since a cheap model that fails and retries is not cheap.

Q52. How do you reduce latency? Stream tokens so time-to-first-token drops even if total time does not. Run retrieval and other independent work in parallel. Cache aggressively. Use a smaller model where quality allows. Cut context length, since prefill scales with it. Set a latency budget per stage and measure against it rather than optimising by intuition.

Q53. How do you handle rate limits and provider failures? Exponential backoff with jitter, request queuing, a circuit breaker, a fallback provider or smaller model, and graceful degradation with a clear user-facing message. Make retried operations idempotent so a retry does not duplicate a side effect. Alert when fallback mode activates rather than letting it become the silent norm.

Q54. What do you monitor for a GenAI system in production? Latency percentiles, token consumption and cost per request, error and fallback rates, retrieval hit rate, refusal rate, user feedback signals, and output quality on a sampled basis. Trace individual requests end to end, because aggregate dashboards will not tell you why one answer was wrong.

Q55. Design a RAG customer support assistant over 500 articles, under 3 seconds, under 5 cents per conversation, 95% grounded. Frame it: ingest with structure-aware chunking, embed, index in pgvector since 500 articles does not justify a dedicated store. Retrieve with hybrid search, rerank the top 20 to 5. Generate with a mid-tier model, context-only instruction, mandatory citations, and a refusal path. Evaluate with a golden set on faithfulness and retrieval recall. Control cost with semantic caching and model routing. Then state the failure modes: no relevant article exists, conflicting articles, and stale content, with a handoff to a human for the first. Testing: whether you start with constraints and evaluation, or with a diagram full of boxes.

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Section 7: Safety, Security and Behavioural (Q56 to Q60)

Q56. What are the main security risks in LLM applications? Prompt injection including indirect injection through retrieved content, sensitive data leakage through prompts and logs, excessive agency where an agent has more permissions than the task needs, insecure output handling when model output reaches a shell or database, and supply chain risk in models and plugins.

Q57. How do you implement guardrails? Layered. Input filtering for PII and injection patterns, tool permission scoping, output validation against schema plus a safety classifier, groundedness checking against retrieved context, and human review for high-stakes actions. No single layer is sufficient, and claiming otherwise is a red flag.

Q58. How do you handle PII in a GenAI pipeline? Detect and redact before the prompt leaves your boundary, avoid logging raw prompts or scrub them at write time, consider self-hosted models where data residency requires it, set retention limits, and document what leaves your infrastructure. For Indian teams this increasingly ties to DPDP compliance obligations, and mentioning that is a credibility marker.

Q59. Tell me about a model that shipped and then misbehaved. Structure: what shipped, how it broke, how you detected it, what you did immediately, and what changed permanently. The permanent change is the point. “We added a groundedness check to the eval suite and gated deploys on it” lands. “We adjusted the prompt” does not.

Q60. How do you stay current in a field this fast? Name a specific sustainable routine: primary sources such as framework release notes and papers over aggregator threads, one project rebuilt when a major version lands, and a filter for what to ignore. Interviewers are testing judgement about what not to chase as much as enthusiasm.

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The Five Answers That Cost Candidates Offers

• “We tested it manually.” Says you have never caught a regression.
• Leading with framework names. Boxes and arrows with no evaluation story stalls at the design round.
• Reaching for the heavyweight tool immediately. Pinecone at 500 documents, multi-agent for a linear task, fine-tuning before trying retrieval. Over-engineering reads as inexperience, not ambition.
• Claiming hallucinations can be eliminated. They can be reduced, detected and gracefully degraded. Absolutes signal theory over practice.
• No numbers anywhere. Chunk size, latency achieved, cost per conversation, accuracy delta. Specifics are the difference between having built something and having read about it.

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A Four-Week Preparation Plan

Week	Focus	Concrete output
1	Foundations	Explain attention on a whiteboard from memory. Implement cosine similarity and a chunker in plain Python
2	RAG depth	Build a RAG service over your own documents with hybrid search and reranking. Record chunk size, latency, cost
3	Evaluation and cost	Add a 100-question golden set, faithfulness scoring, and a regression run. Instrument token cost per query
4	Design and delivery	Talk through three system design prompts aloud with a timer. Write one evaluation story and one failure story in STAR form

The week 3 work is what most candidates skip and what most interviewers now weight highest. Do it even if the portfolio looks finished without it.

Useful adjacent practice: Machine learning tutorial for the fundamentals round and the Python tutorial for the coding round, since almost every GenAI loop still includes both.

Where to Build the Depth These Questions Assume

Reading answers is enough to pass a screen. Passing the design and evaluation rounds requires having built and broken something.

• Certificate Programme in Generative AI: Move past simple prompting to build, fine-tune, and ship production systems with IIT Delhi backing.
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The Bottom Line

The gen AI interview questions that decide outcomes are not the ones with textbook answers. Any candidate can define RAG. Far fewer can explain why they chose 800-token chunks, what their retrieval recall was, how they caught a regression before it shipped, and what the system does when the vector store is unavailable.

Prepare the fundamentals so they are automatic, then spend your remaining time on the two things almost everyone skips: an evaluation harness you built yourself, and honest numbers from a system you have actually run.

Build that depth with the Certificate Programme in Generative AI, or pair your practical engineering skills with an advanced executive credential from a premier institute like IIT Delhi through Varsity.

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