 you probably already know that the interview questions have changed alot compared to even a year ago. Companies are no longer just asking about transformers and attention mechanisms. They want to know if you can build production systems, work with real tools, and design architectures that actually work in the real world.

I've been through multiple Gen-AI developer interviews myself. And I've also taken interviews. Based on that experience, I've compiled 

In this blog, I'm sharing 10 of the most important questions with detailed answers. These are the ones that I've seen trip up even experienced developers. For the complete list of all 60 questions with answers, you can 

This is probably the most common question in 

 right now. And alot of candidates give a textbook answer without really understanding the tradeoffs.

 means you keep your LLM as it is and feed it relevant documents at query time. The model reads the documents and generates an answer based on them. You're not changing the model's weights at all.

 means you actually retrain the model (or parts of it) on your own data. The model's weights change. It "learns" your domain.

 When your data changes frequently (like company docs, support tickets, product catalogs). When you need the model to cite sources. When you don't have the budget or GPU resources for fine-tuning. When you need to get something working in days, not weeks.

 When you need the model to learn a specific style or tone (like writing in your brand voice). When the task is very specialized and the base model keeps getting it wrong even with good context. When latency matters and you can't afford to retrieve documents on every call.

In 2026, most production systems use RAG as the default approach and only fine-tune when RAG alone isn't enough. The cost and complexity of fine-tuning is significantly higher, so companies prefer to avoid it unless there's a clear ROI.

 Don't just explain the difference. Talk about a scenario where you'd combine both. For example, fine-tune a model to follow your company's response format, then use RAG to give it access to up-to-date product information.

 works end-to-end. What are the components ?

A complete RAG pipeline has two phases: the indexing phase (offline) and the retrieval + generation phase (online).

 First, you load your documents. Could be PDFs, web pages, database records, whatever. Then you split them into smaller chunks. The chunk size matters alot here, typically between 500-1000 tokens works well but it depends on your use case. Then you generate embeddings for each chunk using an embedding model (like OpenAI's text-embedding-3-large or a local model like BGE). Finally, you store these embeddings in a vector database like Pinecone, Qdrant, or Weaviate.

 When a user asks a question, you first embed their query using the same embedding model. Then you search the vector database to find the most similar chunks (usually top 5-10). Then you stuff these chunks into the LLM's prompt along with the user's question. The LLM reads the context and generates an answer.

 The follow-up questions are where it gets interesting. They'll ask about chunking strategies (recursive, semantic, agentic), re-ranking (using a cross-encoder after initial retrieval), hybrid search (combining keyword search with vector search), and how you evaluate the pipeline's quality (using frameworks like RAGAS).

Want to build a complete production RAG pipeline from scratch? In 

, we spend an entire sprint building enterprise-grade RAG systems with evaluation, hybrid search, and deployment. 

 is a linear pipeline. Query goes in, chunks come out, answer gets generated. There's no decision making involved. The system can't decide "these chunks are bad, let me try a different search" or "I should also check the web for this."

Agentic RAG adds an AI agent on top of the RAG pipeline. The agent acts like a smart coordinator that can make decisions at each step.

Here's what the agent can do that traditional RAG cannot:

It can look at the retrieved chunks and decide if they're actually relevant. If they're not, it rewrites the query and tries again. It can choose between different retrieval sources. Maybe it searches the vector database first, and if the results are weak, it falls back to web search. It can grade its own answers for hallucination. If the generated answer doesn't match the source documents, it regenerates. It can break a complex question into sub-questions and retrieve information for each one separately.

The most common agentic RAG patterns in 2026 are Corrective RAG (checks document relevance and rewrites queries), Self-RAG (the model grades its own output), and Adaptive RAG (dynamically chooses the retrieval strategy based on the query type).

 Draw the flow on a whiteboard. Show the loops and decision points. This is what separates a good answer from a great one.

4. What is LangGraph and why would you use it instead of plain LangChain?

LangChain is a high-level framework for building LLM applications. It gives you building blocks like chains, agents, and tools. Its great for prototyping and getting things working fast.

LangGraph is a lower-level orchestration framework from the same team. It lets you model your AI workflow as a graph where each step is a node and the connections between steps are edges. A shared state object flows through the graph.

You'd use LangGraph when you need: loops (the agent tries something, checks if it worked, and tries again if not), branching logic (different paths based on different conditions), state persistence (the system remembers where it was even if the server restarts), human-in-the-loop (pausing execution for a human to review or approve), and multi-agent coordination (multiple agents working together on a task).

 function actually runs on LangGraph under the hood. So in production, most teams use LangChain for the integrations (connecting to LLMs, tools, databases) and LangGraph for the workflow orchestration.

MCP is an open protocol released by Anthropic in November 2024 that standardizes how AI applications connect to external tools and data sources. Think of it like how USB-C standardized charging cables. Before USB-C, every phone had a different charger. Before MCP, every LLM had a different way of connecting to tools.

With MCP, you build a server once (for example, a server that reads from your company's database), and it works with Claude, ChatGPT, Cursor, VS Code Copilot, and any other MCP-compatible client. No rewiring needed.

MCP has three primitives: Tools (actions the AI can perform, like sending an email), Resources (data the AI can read, like a file or database record), and Prompts (reusable templates for interactions).

It uses JSON-RPC 2.0 for communication and supports two transports: stdio for local servers and Streamable HTTP for remote servers.

 MCP shows up in 30-40% of Gen-AI job descriptions in 2026. Companies want developers who can build MCP servers that connect their AI systems to internal tools. If you can demonstrate that you've actually built an MCP server, it puts you ahead of most candidates.

6. How do you evaluate a RAG pipeline? What metrics do you use?

This question catches alot of candidates off guard because they focus on building the pipeline but never think about how to measure if it's actually working well.

The most popular evaluation framework in 2026 is RAGAS (Retrieval-Augmented Generation Assessment). It measures several things:

 Does the generated answer actually match what the source documents say? Or is the model hallucinating information that isn't in the retrieved context?

 Is the generated answer actually answering the user's question? Sometimes the model generates a technically correct response that doesn't address what was asked.

 Are the retrieved chunks actually relevant to the question? If you retrieved 10 chunks but only 2 are relevant, your precision is low.

 Did you retrieve all the relevant information? Maybe there were important chunks in your database that the retrieval step missed.

Beyond RAGAS, you should also track: latency (how long does end-to-end response take), chunk hit rate (what percentage of retrieved chunks are actually used in the answer), and user feedback (thumbs up/down from actual users).

 Mention that you set up evaluation as a CI/CD step. Every time you change chunking strategy, embedding model, or retrieval parameters, you run the evaluation suite automatically. This shows production mindset.

. How do they work in the context of RAG?

Embeddings are numerical representations of text. You take a sentence or paragraph and convert it into a list of numbers (a vector). Similar texts end up with similar vectors, and different texts end up with different vectors.

For example, "I love dogs" and "I adore puppies" would have very similar embeddings because they mean almost the same thing. But "I love dogs" and "The stock market crashed" would have very different embeddings.

In RAG, embeddings are used for two things. First, during indexing, you embed all your document chunks and store them in a vector database. Second, during retrieval, you embed the user's query and find the most similar document chunks by comparing their vectors.

The similarity is usually measured using cosine similarity (the angle between two vectors) or dot product.

Popular embedding models in 2026 include OpenAI's text-embedding-3-large (1536 dimensions), Cohere's embed-v4, and open-source options like BGE-M3 from BAAI.

 Talk about the tradeoffs between different embedding models. Larger dimensions give better accuracy but slower search. Multilingual models work across languages but might be slightly less accurate in English. And always mention that you should use the same embedding model for both indexing and querying, otherwise the vectors won't be comparable.

8. How would you handle a situation where your RAG system gives wrong or hallucinated answers?

This is a common production problem and interviewers want to see your debugging approach.

First, figure out where the problem is. Is it a retrieval problem (wrong chunks being retrieved) or a generation problem (right chunks but wrong answer)?

 Check your chunking strategy. Maybe chunks are too big and contain mixed topics. Try smaller, more focused chunks. Check your embedding model. Maybe its not capturing the semantic meaning well for your domain. Try a different model. Add hybrid search. Combine vector search with keyword (BM25) search. Sometimes the answer is in a document that uses different vocabulary than the query. Add a re-ranking step. Use a cross-encoder to re-score the top results before passing them to the LLM.

 Improve your prompt. Add explicit instructions like "Only answer based on the provided context. If the context doesn't contain enough information, say you don't know." Add citation requirements. Ask the model to reference which document each fact came from. Add a hallucination checker. Use a separate LLM call to verify if the answer is grounded in the provided context.

 Give a real example if you can. "In my project, the RAG system was hallucinating company policies. The issue turned out to be chunk overlap, where two contradictory policies were in the same chunk. Splitting them into separate chunks fixed the problem."

 (also called semantic search) converts text into embeddings and finds similar vectors. It understands meaning, so "automobile" and "car" would match even though the words are different. But it can struggle with exact matches, like searching for a specific product code or error message.

 (like BM25 or TF-IDF) looks for exact word matches. Its great for specific terms, names, codes, and technical terms. But it fails when the user uses different words than what's in the documents.

 combines both. You run a vector search and a keyword search in parallel, then merge the results using a technique called Reciprocal Rank Fusion (RRF). This gives you the best of both worlds.

 use hybrid search because it handles both semantic queries ("how do I reset my password?") and specific queries ("error code ERR-4052") well.

 If they ask about implementation, mention that Pinecone, Qdrant, and Weaviate all support hybrid search natively. You don't have to build it from scratch.

10. Design a multi-agent system for a customer support chatbot. Walk me through the architecture.

This is a system design question that tests your ability to think at a higher level.

 This is the entry point. It receives the customer's message and decides which specialist agent should handle it. It routes based on the intent of the message.

 Handles factual questions about products, policies, returns, etc. Uses a RAG pipeline connected to the company's knowledge base. Built with LangGraph for state management.

 Handles order-related queries. Has tools to check order status, process returns, and update shipping info. Connects to the order management system via MCP.

 When a query is too complex or the customer is frustrated, this agent prepares a summary and hands off to a human support agent. Includes conversation context and suggested resolution.

The state flows through a LangGraph graph.

 Each agent is a node. The supervisor decides which node to route to based on the message. After each agent responds, the supervisor checks if the customer's issue is resolved. If not, it can route to a different agent or escalate.

 Use MemorySaver or PostgresSaver for state persistence so the conversation survives server restarts. Add human-in-the-loop for actions that involve refunds above a certain amount. Log everything to LangSmith for debugging and quality monitoring.

These are the exact kinds of system design questions you'll face in Gen-AI interviews. In 

, we do 5 mock interviews with written feedback so you practice answering these questions before the real interview. 

These 10 questions cover the core topics, but there are 40 more that come up regularly in Gen-AI developer interviews. Topics like AWS Bedrock architecture, Docker deployment for AI apps, LangSmith observability, chunking strategies in depth, token management, prompt engineering patterns, and more.

Download the complete 50 Gen-AI Interview Questions PDF for free

 - includes detailed answers for every question, code snippets where relevant, and tips on what interviewers are really looking for.

If you're serious about cracking a Gen-AI developer role, preparation is everything. Its not enough to just know the tools. You need to be able to explain your thinking, draw architectures on a whiteboard, and talk about production tradeoffs.

Preparing for Gen-AI developer interviews?

 sprint where you build 5 production projects and go through 5 mock interviews with personalized written feedback. Its built specifically for working developers with 2+ years of experience who want to break into Gen-AI roles. 

Prateek Mishra, Senior Gen-AI Developer  

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            font-size: 16px;
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            color: white;
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            justify-content: center;
            align-items: center;
            font-size: 16px;
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        .content-temp-title-image {
            width: 25px !important;
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        .content-temp-title {
            color: #006d77;
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