Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A large enterprise is evaluating gen AI for internal knowledge management. They need to ensure accuracy and reduce hallucinations. Which strategy is most effective?
⚠ Common exam trap
Google Cloud often tests the misconception that fine-tuning is the universal solution for domain adaptation, but the trap here is that fine-tuning does not provide a dynamic, verifiable knowledge source, whereas RAG explicitly decouples knowledge storage from generation, enabling real-time updates and source attribution.
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)
Retrieval-Augmented Generation (RAG) is the most effective strategy because it grounds the model's responses in an external, authoritative knowledge base, retrieving relevant documents at inference time to provide factual context. This directly reduces hallucinations by ensuring the generated output is based on retrieved evidence rather than relying solely on the model's parametric memory, which is critical for enterprise knowledge management where accuracy is paramount.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune a model on domain-specific data
Why it's wrong here
Fine-tuning bakes domain style and terminology into weights but cannot ground answers in the current document corpus, so hallucinations persist. It is tempting because it genuinely improves tone and task format; it would be right where the goal is domain-specific behaviour, not factual retrieval accuracy.
- ✗
Increase model temperature
Why it's wrong here
Raising temperature increases sampling randomness, producing more varied and less deterministic output, which worsens fabrication rather than reducing it. Temperature tuning is for creative or diverse generation; accuracy-critical knowledge management needs low temperature plus retrieval grounding.
- ✓
Use Retrieval-Augmented Generation (RAG)
Why this is correct
RAG retrieves relevant documents and conditions the model on them, dramatically reducing hallucinations.
- ✗
Use a larger model without customization
Why it's wrong here
Scaling parameters improves fluency and reasoning, yet the model still answers from frozen training data with no access to the enterprise corpus, so unsupported claims remain. Larger models suit general capability gains; grounding accuracy requires retrieval of the actual source documents at query time.
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