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Generative AI Leader Practice Question: Building a chatbot that must answer questions…
A company is building a chatbot that must answer questions based on a large internal knowledge base that is updated weekly. They want to avoid retraining the model frequently. Which technique should they use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Retrieval-Augmented Generation (RAG) with a vector database
RAG retrieves relevant documents at inference time, keeping answers up-to-date without retraining. Fine-tuning would require frequent retraining; prompt engineering alone cannot incorporate new knowledge.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Retrieval-Augmented Generation (RAG) with a vector database
Why this is correct
RAG retrieves relevant passages from the vector database at query time and supplies them as context, so weekly knowledge base updates only require re-indexing rather than retraining. This satisfies the constraint of avoiding frequent model retraining while keeping answers current.
- ✗
Use prompt engineering to instruct the model to ignore outdated information
Why it's wrong here
Prompt instructions cannot give the model access to documents it has never seen, so answers on updated content remain fabricated or stale. It is tempting because prompt engineering is cheap and needs no retraining, but it would be correct for steering tone, format or reasoning style.
- ✗
Increase the model's context window and include all documents in the prompt
Why it's wrong here
A context window cannot hold an entire large, weekly-changing knowledge base, and stuffing documents in wastes tokens while missing updates. It is tempting because context expansion avoids retraining, but it would be correct for small, static document sets that fit within the window.
- ✗
Fine-tune the model weekly on the updated knowledge base
Why it's wrong here
Weekly fine-tuning is itself frequent retraining, directly contradicting the requirement, and it risks catastrophic forgetting between runs. It is tempting because fine-tuning embeds domain knowledge, but it would be correct when adapting stable behaviour or style rather than a knowledge base that changes weekly.
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