Reranking for RAG —Precision Context Selection Before Generation

I will implement reranking specifically optimised for RAG context selection — ensuring the most relevant retrieved chunks are included in the LLM's context window rather than the most similar by embedding distance, reducing hallucination from irrelevant context and improving answer accuracy from precisely selected context. RAG reranking has different optimisation objectives from search reranking — search reranking optimises for result order (what users click first), while RAG reranking optimises for context precision (which chunks most inform the answer), requiring reranking models and prompts calibrated to relevance for answer generation rather than relevance for user selection.

The implementation covers cross-encoder reranking calibrated for answer-relevance rather than general relevance, context budget management including only chunks above a relevance threshold, reranking quality evaluation using answer faithfulness metrics, and RAGAS score comparison before and after reranking.