Retrieval-Augmented Generation (RAG) systems can sometimes pull in irrelevant or noisy context that degrades the quality of the generated response. To prevent this, consider the following strategies:
- Improve retrieval precision – Use hybrid retrieval (combining sparse and dense methods) to better match queries with relevant documents.
- Re-rank retrieved chunks – Apply a cross-encoder or similar model to score and filter out low-relevance passages before passing them to the LLM.
- Set retrieval thresholds – Discard chunks whose similarity score falls below a certain threshold.
- Optimize chunk size and overlap – Fine-tune these parameters to ensure each chunk contains self-contained, meaningful information.
- Use query rewriting – Clarify ambiguous or multi-intent queries before retrieval.
By implementing these techniques, you can significantly reduce noise and improve the accuracy of RAG outputs.