These are GenAI questions of the kind TCS actually asks — the patterns that keep coming back in TCS's GenAI and AI-project interview rounds, where the focus is on applying LLMs sensibly for clients rather than research depth. My advice: treat this page as a mock interview. Say every answer out loud before revealing it — GenAI rounds are conversations, not written tests. If an answer surprises you, the lesson behind it is linked at the bottom.
TCS GenAI concept questions
Differentiate between AI, machine learning, deep learning and generative AI — where does each sit relative to the others?
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What is a prompt, and why is prompt design treated as an engineering task in GenAI projects rather than casual typing?
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Your team wants to use a public LLM API in a client project. What data-privacy concerns must you raise, and how are they typically addressed?
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TCS GenAI applied & hands-on questions
A client wants customer emails converted into structured records. Write the prompt you'd send (with the email as context) so the model returns strict JSON with fields customer_name, product, complaint, urgency (low/medium/high). State one thing your code must still do after receiving the response.
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How to use this page: TCS rarely asks a question you've never seen — they ask standard GenAI concepts and then push one level deeper ("why?", "what would you do if..."). Master the concept in the GenAI course lessons, and the follow-up stops being scary.
Keep practising: How LLMs Work, Prompting Basics, RAG and Fine-tuning vs RAG cover what most TCS GenAI rounds test — and the TCS NQT exam guide covers the rest of their selection process.