AIF-C01 Applications of Foundation Models Practice Question

A generative AI application occasionally produces factually incorrect responses. The team has already tried prompt engineering and increasing the temperature parameter. Which next step is MOST effective to improve factual accuracy?

⚠ Common exam trap

AWS often tests the misconception that reducing temperature or using a larger model directly fixes factual accuracy, when in fact these methods address output randomness and capacity, not the root cause of hallucination, which is lack of grounded knowledge.

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

✓

Implement a Retrieval Augmented Generation (RAG) pipeline

Retrieval Augmented Generation (RAG) is the most effective next step because it grounds the model's responses in a verified external knowledge base, directly addressing factual inaccuracies without requiring retraining. Unlike prompt engineering or temperature adjustments, RAG provides real-time access to authoritative documents, reducing hallucinations by constraining the model's output to retrieved evidence.

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 a larger foundation model
Why it's wrong here
A larger foundation model may raise general capability but offers no retrieval of authoritative sources, so factual errors persist. It is tempting because scaling parameters improves many benchmarks, which suits broad capability upgrades, yet it would be correct only where the gap is model capability rather than missing grounded context.
✗
Fine-tune the model on company data
Why it's wrong here
Fine-tuning teaches style, format and domain tone, not factual grounding; it can even reinforce confident errors. It is tempting because it customises behaviour on proprietary data, and would be right for adapting output format or terminology, not for correcting hallucinated facts, which retrieval grounding addresses.
✗
Reduce the temperature to 0
Why it's wrong here
Temperature controls sampling randomness, so zero makes output deterministic but does not supply missing knowledge; a confidently wrong answer stays wrong. It is tempting because lower temperature reduces creative variation, which suits reproducible classification or extraction tasks, not grounding responses in verifiable facts.
✓
Implement a Retrieval Augmented Generation (RAG) pipeline
Why this is correct
RAG retrieves relevant documents from a knowledge base and injects them into the prompt, grounding generation in verifiable source content. Prompt engineering and temperature tuning cannot supply missing facts, so retrieval directly targets factual accuracy.

Go deeper

AIF-C01 Applications of Foundation Models Practice Question

Why each option matters

Option-by-option breakdown

Related to this question