These are GenAI questions of the kind Wipro actually asks — the patterns that keep coming back in Wipro'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.
Wipro GenAI concept questions
What is the difference between GPT (the model) and ChatGPT (the product)? Why does the distinction matter?
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Explain embeddings to a project manager, and give one concrete business use case beyond chatbots.
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A teammate says 'our chatbot forgot the conversation from 30 messages ago — the model must be broken.' What is actually happening and how do production systems handle it?
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Wipro GenAI applied & hands-on questions
Before running it, predict the relative order of token counts this code prints — which line costs the most tokens and why?
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import tiktoken
enc = tiktoken.get_encoding("cl100k_base")
lines = [
"the cat sat on the mat", # A: common English words
"internationalization considerations", # B: two long rare words
"mujhe kal chhutti chahiye boss", # C: romanized Hindi
]
for line in lines:
print(len(enc.encode(line)), line)How to use this page: Wipro 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 Wipro GenAI rounds test — and the Wipro exam guide covers the rest of their selection process.