Are you struggling with 1M+ token windows? Learn 5 RAG techniques to beat 'Lost in the Middle' and slash costs with context caching.

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Long-context LLMs like Gemini Pro and Claude Opus offer million-token windows but introduce two key problems: the 'Lost in the Middle' attention failure and high processing costs. Five practical techniques address these challenges: (1) reranking retrieved documents and placing the most relevant at the start and end of the prompt; (2) context caching to avoid reprocessing large static documents on every query; (3) dynamic chunking with metadata filters for two-step hybrid retrieval; (4) combining vector and keyword search via Reciprocal Rank Fusion; and (5) query expansion using a lightweight LLM to generate hypothetical document snippets that broaden retrieval coverage.

5 Techniques for Efficient Long-Context RAG

1. Implementing a Reranking Architecture to Fight “Lost in the Middle”

2. Leveraging Context Caching for Repetitive Queries

3. Using Dynamic Contextual Chunking with Metadata Filters

4. Combining Keyword and Semantic Search with Hybrid Retrieval

5. Applying Query Expansion with Summarize-Then-Retrieve