These are RAG questions of the kind TCS actually asks — the patterns that keep coming back in TCS's AI project interviews, where what matters is applying RAG sensibly for clients rather than research depth. Treat this page as a mock interview: say every answer out loud before revealing it. If one surprises you, the lesson behind it is linked at the bottom.
TCS RAG concept questions
Explain RAG to a client who is not technical, and tell them in one line why it is safer than letting ChatGPT answer directly.
Asked in
In a RAG project, what could go wrong if documents are split into chunks purely by a fixed character count?
Asked in
A client asks whether their confidential documents are safe if you build a RAG chatbot using a public LLM API. What do you tell them?
Asked in
TCS RAG applied & hands-on questions
A client wants a RAG bot over 300 HR policy PDFs, used by 5,000 employees. Give your five-step build plan with one deliverable per step.
Asked in
How to use this page: TCS rarely asks something you've never seen — they ask a standard RAG concept and then push one level deeper ("why?", "what would you do if..."). Master the concept in the RAG course lessons, and the follow-up stops being scary.
Keep practising: What Is RAG?, Chunking, Retrieval Techniques and Evaluating RAG cover what most TCS RAG rounds test — and the TCS NQT exam guide covers the rest of their selection process.