# Retrieve And Rerank

> Retrieve top-k chunks for a question, rerank with a cross-encoder, return the top-n with scores.

- Skill: `jcaiagent7143-ui/retrieve-and-rerank` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jcaiagent7143-ui/retrieve-and-rerank`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jcaiagent7143-ui/retrieve-and-rerank/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: jcaiagent7143-ui (https://skillmd.com/u/jcaiagent7143-ui)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/jcaiagent7143-ui/retrieve-and-rerank

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# Retrieve and Rerank

## Steps

1. **Embed the question** with the same model used during ingestion.
2. **Vector search** for `top_k` candidates by cosine similarity.
3. **Rerank** with a cross-encoder (e.g. `bge-reranker-base` or LLM-judge
   prompt) — the bi-encoder score is recall, the cross-encoder score is
   precision.
4. **Return the top `top_n`** with their reranker score.
5. **Log retrieval** to session memory (`retrieved_chunks` per turn) so
   the validator can confirm cited chunks were actually retrieved.

## Validation

`harness verify --check citations` confirms every chunk cited in the
answer is in the session's `retrieved_chunks` list. If you skip the
session-memory log step, this validator will fail every run.

## Failure modes to avoid

- **Skipping the rerank** — bi-encoder alone returns plausible-but-irrelevant
  chunks; the answerer then hallucinates a justification.
- **Returning chunks the user can't see** — if a chunk was indexed from a
  forbidden path, the retriever must skip it. Check `MEMORY.md`.
- **No empty-result handling** — if `top_n == 0`, the answerer must say so;
  don't invent.