This article discusses strategies for managing the context window limitations of large language models, particularly within retrieval-augmented generation (RAG) systems. It explores techniques such as token budgeting, data compression, and evidence ordering to optimize performance and mitigate issues like the "lost in the middle" problem. The piece is presented as the twelfth part of a RAG engineering series. AI
IMPACT Improves efficiency and accuracy of RAG systems by addressing context window limitations.
RANK_REASON The item is a technical article discussing methods for improving AI model performance, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
- Context Window Management
- data compression
- Evidence Ordering
- retrieval-augmented generation
- token-budgets
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