Context Ranking

Context ranking is the process of ordering retrieved text chunks so the most relevant, diverse, and useful information rises to the top. In any retrieval pipeline, the initial search returns a broad set of candidates -- many of which are only tangentially related to the query. Ranking transforms this unordered candidate set into a prioritized list, enabling downstream steps (context assembly, prompt construction) to select the best material and discard the rest. Effective ranking is the difference between a grounded, precise answer and a vague, off-topic one.

Workflow

1. Collect Candidate Chunks: Gather the initial set of retrieved chunks from the search layer. This is typically the top-k results (k = 15-30) from a vector search, keyword search, or hybrid search. Each chunk arrives with a preliminary score (e.g., cosine similarity or BM25 score) and source metadata.

2. Apply First-Stage Scoring: Score each candidate with a fast, lightweight algorithm. BM25 is the standard choice for keyword relevance; cosine similarity between the query embedding and chunk embedding is the standard for semantic relevance. In hybrid pipelines, compute both scores and combine them using Reciprocal Rank Fusion (RRF) or a weighted linear combination. This stage is meant to be fast and run over all candidates.

3. Rerank with a Cross-Encoder: Pass the top candidates (typically 15-25) from the first stage through a cross-encoder reranker. Unlike bi-encoder embeddings that score query and document independently, a cross-encoder processes the query and chunk together with full attention, producing much more accurate relevance scores. Models like Cohere Rerank, bge-reranker-v2-m3, or ColBERTv2 are commonly used. This step is slower but dramatically improves precision.

4. Apply Diversity Selection: After reranking, the top results may cluster around a single subtopic, leaving other aspects of the query uncovered. Apply Maximal Marginal Relevance (MMR) or a similar diversity algorithm to penalize chunks that are too similar to already-selected chunks. This ensures the final ranked list covers the breadth of the query, not just its most obvious interpretation.

5. Assign Final Scores and Rank: Combine the reranker relevance score with the diversity penalty and any domain-specific boosting signals (e.g., recency boost, source authority weight) into a final composite score. Sort chunks by this composite score in descending order. The top-n chunks (n = 3-7) form the final ranked context to be injected into the prompt.

6. Attach Metadata and Confidence: Annotate each ranked chunk with its final score, source path, and a confidence tier (high / medium / low). This metadata helps the downstream prompt assembly step decide how to present the context and allows the model to calibrate its confidence when citing sources.

Key Concepts