What is context window management in RAG and why does it matter?

Context window management determines how retrieved content is formatted and arranged within the LLM prompt, directly impacting answer quality. The total prompt includes the system instruction, retrieved context chunks, conversation history, and the user query, all competing for limited context space. Key strategies include context compression using LLMs or specialized models to summarize retrieved chunks before insertion, relevance-ordered context placing the most relevant chunks first since LLMs attend more to early and late context due to the lost-in-the-middle phenomenon, deduplication removing overlapping content from multiple retrieved chunks, dynamic context allocation adjusting the number of chunks based on query complexity, and reserving sufficient space for the model to generate a complete response. Poor context management leads to truncated context, wasted tokens on irrelevant information, or insufficient generation space.