These are GenAI questions of the kind HCLTech actually asks — the patterns that keep coming back in HCLTech'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.
HCLTech GenAI concept questions
What is fine-tuning, and give one client scenario where it IS the right call plus one where it is NOT (with the better alternative).
Asked in
What is a vector database, what one operation is it optimized for, and name two examples.
Asked in
What is a multimodal model? Describe one enterprise use case where multimodality is essential, not optional.
Asked in
HCLTech GenAI applied & hands-on questions
Sketch the API call for extracting structured data from invoice text into JSON with fields invoice_no, vendor, date, total. Choose temperature and response format deliberately, and say what your code does if the JSON fails to parse.
Asked in
How to use this page: HCLTech 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 HCLTech GenAI rounds test — and the HCLTech exam guide covers the rest of their selection process.