The best fix is Use structured prompt engineering. By providing clear instructions about the subject domain, desired headings or a JSON schema, constraints, and a few examples you can guide Amazon Bedrock to produce organized and on-topic study guides for middle school math and history.

Structured prompt engineering works because it tells the model the exact output format and the scope of content you expect. You can include explicit section headers, bullet templates, example inputs and outputs, and validation rules so the model consistently follows the required layout and stays aligned with the requested topic. Few-shot examples and schema enforcement reduce ambiguity and make outputs reproducible across prompts.

Reduce the temperature setting mainly reduces randomness and can make language more predictable but it will not enforce format or prevent topical drift. Lowering temperature alone rarely creates the consistent headings and structure you need.

Amazon Bedrock Knowledge Bases can help ground answers in domain content and improve factual relevance but they do not by themselves impose output formatting. You still need explicit prompt instructions to get a reliably structured study guide.

Fine-tuning can shape tone and style over many examples but it is more expensive and slower to iterate. Fine-tuning is usually unnecessary when well designed prompts and examples can achieve the required structure and topical alignment quickly.

Embeddings with retrieval augmentation improves topical relevance by supplying relevant source material but it does not guarantee organized output. Retrieval is a strong complement to structured prompts when factual grounding is important, yet it does not replace the need for explicit output format instructions.

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