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The Best LLMs for RAG: Recall, Context & Caching Audit

Retrieval-Augmented Generation (RAG) inserts external context into prompts. To choose the right model, evaluate context size, recall accuracy, and prompt caching support. Let's compare the top options.

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1. Recall Accuracy (lost in the middle)

Large context windows can suffer from recall degradation. Claude 3.5 Sonnet and GPT-4o maintain high recall accuracy, successfully retrieving facts buried in long prompts.

2. Caching Support for Long Documents

RAG systems send large documents. Claude's 90% caching discount and Gemini's 50% discount are highly effective at reducing input token costs.

3. Budget Alternatives: GPT-4o-mini and Gemini Flash

For high-volume operations, GPT-4o-mini and Gemini 1.5 Flash support prompt caching, offering a cost-effective path for document processing.

Frequently Asked Questions

Should I choose Claude or Gemini for RAG?

For massive context needs (up to 2M tokens), Gemini is essential. For maximum recall accuracy and code generation, Claude is superior.

Does prompt caching work with dynamic RAG search?

Caching requires matching prefixes. Group static instructions and core documents at the beginning, and place dynamic query text at the end to trigger cache hits.