How to Trim RAG Context to Only What the Answer Requires

The Kapa.ai blog post examines how retrieval‑augmented generation (RAG) often includes more context than needed. It argues that excess material can dilute answer relevance and increase processing cost. The author proposes a pruning strategy that isolates only the portions directly required for the final answer. The method involves evaluating each retrieved segment against the question’s intent. Segments that do not contribute to the answer are discarded before generation. This selective approach reduces token usage while preserving answer quality. The post includes practical examples illustrating the pruning workflow. Readers are encouraged to apply the technique to improve efficiency in their own RAG pipelines.