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Read More →Sample interview questions and answers from Uncodemy — covering LLM basics, tokens and context windows, prompting, hallucinations, RAG versus fine-tuning, evaluation and responsible use.
All three PDFs are free to download — no form, no sign-up.
Three PDFs, each covering a different level of interview preparation. Click the sticky download button to get started.
The complete set of interview questions in one PDF: LLM fundamentals, tokens and context windows, prompting techniques, hallucination mitigation, RAG vs fine-tuning, evaluation and responsible AI. For anyone preparing end-to-end for a GenAI interview.
Foundational interview questions with clear answers: what an LLM is, how tokens and context windows work, prompt design and its limits, why hallucinations happen, and how to reduce them. For freshers and junior-level GenAI roles.
Advanced interview questions for experienced candidates: when to use RAG versus fine-tuning, embedding and vector search, evaluation metrics, privacy and PII handling, bias mitigation and production safety. For mid-level and senior GenAI roles.
Across companies and roles, Generative AI interviews tend to test the same core areas. Prepare these five and you'll cover most of what comes up.
Transformers, next-token prediction, embeddings, context windows, and the difference between an LLM and a traditional ML model. Interviewers want to hear it in your own words.
Zero-shot, few-shot, chain-of-thought, structured output, and when prompting alone isn't enough — leading into retrieval or fine-tuning.
When to retrieve, when to train, cost and latency implications, and how production systems often combine both. Expect a real scenario question.
How you measure whether an LLM app works, what metrics you use, and concrete strategies to detect and reduce hallucinations in production.
PII handling, data retention, prompt injection, bias mitigation, guardrails and human-in-the-loop patterns. Interviewers increasingly test whether you can talk about risk as fluently as capability.
These are the kinds of questions the PDFs cover in detail. Read each answer, then practise explaining it in your own words with an example.
A model trained on very large amounts of text to predict the next token, which lets it generate and understand language. It is built on the transformer architecture and is adapted to tasks through prompting, retrieval or fine-tuning.
Tip: mention transformers + adaptation methodsTokens are small chunks of text that the model reads and writes. The context window is the maximum number of tokens the model can consider in one request, including both your input and its output.
Tip: give an example of a token countModels generate plausible text rather than verified facts. Reduce hallucinations by grounding answers in retrieved documents, instructing the model to say when it does not know, lowering randomness where suitable and verifying important outputs.
Tip: name two or three concrete mitigationsUse RAG when knowledge changes or is private, because it supplies information at question time. Use fine-tuning to change style, format or behaviour. Many systems start with prompting and RAG before considering fine-tuning.
Tip: mention cost, latency and maintenance trade-offsTemperature controls randomness in token selection. Lower values give more predictable output; higher values give more varied output.
Tip: give a use case for each end of the rangeAn embedding is a numerical vector representation of text, capturing semantic meaning. It is used for semantic search, clustering, recommendations and as the retrieval foundation for RAG systems.
Tip: connect embeddings to RAG directlyUse a mix of automated metrics (accuracy, relevance, hallucination rate), LLM-as-a-judge scoring, and human evaluation on a test set. Build the evaluation set before optimising, and re-run it after every change.
Tip: name the metrics you actually trackDetect and redact PII before sending data to the model, avoid logging sensitive content, use enterprise or self-hosted models where needed, and add data-retention controls. Document these decisions as part of the system design.
Tip: mention both prevention and detectionChain-of-thought prompting asks the model to reason step by step before giving a final answer. It helps on multi-step reasoning tasks but adds latency and tokens, so it's worth using only where accuracy matters more than speed.
Tip: mention the latency trade-offCompare on task accuracy, latency, cost per token, context window size, privacy requirements and available tooling. Run a small evaluation on your own data rather than choosing by public benchmark alone.
Tip: always anchor on your own evaluationThese are the habits that separate candidates who can explain concepts clearly from those who can only recite definitions.
Every PDF in this download set is built around the same core topics and tools used in real Generative AI interviews.
These PDFs are designed for anyone preparing for a Generative AI interview — whether it's your first role or a move into a more senior position.
Graduates preparing for their first Generative AI or ML interview and needing clear, correct answers on core concepts.
Start with Core Concepts PDFDevelopers transitioning into GenAI roles who already code but need to sharpen their interview answers on retrieval, evaluation and safety.
Start with Complete Bank PDFData scientists adding GenAI to their toolkit and preparing for interviews that test LLM-specific trade-offs.
Start with Advanced PDFWorking professionals moving into AI roles who need a structured, credible way to explain concepts in interviews.
Start with Core Concepts PDFProfessionals preparing for an internal AI role or a promotion interview that now includes GenAI topics.
Start with Complete Bank PDFSenior engineers who need to defend architecture decisions around RAG, fine-tuning and production safety in interviews.
Start with Advanced PDFThese are the roles where Generative AI interview questions from this set most directly apply.
| Role | Core topics tested | PDF to use |
|---|---|---|
| Generative AI Engineer | LLMs, RAG, fine-tuning, evaluation, safety | Complete Question Bank |
| LLM Application Developer | Prompting, tokens, context windows, hallucination mitigation | Complete Question Bank |
| Prompt Engineer | Prompt design, chain-of-thought, few-shot, limits of prompting | Core Concepts |
| RAG Engineer | Embeddings, vector search, RAG vs fine-tuning, evaluation | Advanced |
| AI/ML Engineer | Transformers, LLM fundamentals, evaluation, responsible AI | Complete Question Bank |
| AI Solutions Architect | System design, RAG vs fine-tuning, privacy, guardrails | Advanced |
If your interview is about a month away, this is a simple way to use the PDFs in order.
Read the Core Concepts PDF. Learn to explain LLMs, tokens, context windows, prompting and hallucinations in your own words with an example each.
Read the Advanced PDF for the RAG vs fine-tuning chapter. Sketch one architecture diagram and practise walking through it in 3 minutes.
Study evaluation metrics, hallucination mitigations and PII handling. Prepare two project stories where you measured success and handled a safety concern.
Use the Complete Question Bank PDF as a mock question set. Practise answering out loud and reviewing where your answers slip into jargon without examples.
More study material and guides that pair with these interview questions.
Prefer guided learning? Explore Uncodemy's Generative AI / Agentic AI course and prepare for interviews with real trainers — 100+ live sessions, 160+ learning hours and a 25-hour capstone project.
These PDFs are study material for learning and revision. They are not a certificate, a degree or a promise of a job, and technology changes, so check official documentation for the latest details.
Spot an error? Write to info@uncodemy.com and the trainer team will review it.
Everything you need to know before using these interview questions.
Common topics are how LLMs work, tokens and context windows, prompting, hallucinations, RAG versus fine-tuning, evaluation and responsible use.
RAG gives the model relevant information at question time. Fine-tuning changes the model through extra training. RAG suits changing or private knowledge; fine-tuning suits style and behaviour.
Say that a model can generate confident text that is false or unsupported, and describe mitigations such as grounding in documents, verification and clear refusal behaviour.
Temperature controls how random the model's output is. Lower values are more predictable and higher values are more varied.
No. They are written to cover common concepts. We do not claim any specific company asked them.
Explain each concept in your own words with an example. Be ready to describe a small project end to end. Know the limits and risks as well as the benefits. Use the four-week plan on this page if your interview is a month away.
Most Generative AI engineering roles expect Python familiarity. Prompt engineering and some applied roles are lighter on coding but still test API usage and evaluation fundamentals. The PDFs mark which questions are coding-heavy and which are concept-only.
A small end-to-end project works best: a RAG chatbot on documents you know, a summariser with clear evaluation, or a prompt-based assistant with guardrails. Be ready to explain what you measured, what failed and how you improved it.
Yes. All three Generative AI interview question PDFs on this page are completely free to download. No sign-up, payment or form is required — just click the sticky download button on this page.
Grab the Generative AI interview questions PDF and work through the sample answers at your own pace. All three PDFs are free to download — just use the sticky download button.
