Lesson 635 of 2116
Reranker Evals: The Second Look at Evidence
A reranker can improve local RAG by reordering candidate chunks, but it adds latency and needs measurement.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The operational idea: reranker evaluation
- 2reranker
- 3cross-encoder
- 4top-k
Concept cluster
Terms to connect while reading
Section 1
The operational idea: reranker evaluation
A reranker can improve local RAG by reordering candidate chunks, but it adds latency and needs measurement. In local AI, the model family is only one part of the system. The runtime, file format, serving path, hardware budget, evaluation set, and safety policy decide whether the model becomes useful.
Compare the options
| Layer | What to decide | What can go wrong |
|---|---|---|
| Runtime | reranker evaluation | The model runs, but the workflow is slow or brittle |
| Evaluation | A small task-specific test set | A flashy demo hides routine failures |
| Safety and ops | Permissions, provenance, logging, and rollback | Adding a reranker because it sounds advanced while making the app slower without improving answers. |
Current source signal
Build the small version
Compare answers with no reranker, a small reranker, and a stronger reranker on the same question set.
- 1Define the user task in one sentence.
- 2Choose the smallest model and runtime that might pass that task.
- 3Run one happy-path prompt and one failure-path prompt.
- 4Record speed, memory pressure, output quality, and the exact reason for any failure.
- 5Write the operating rule you would give a non-expert user.
A local-model operations sketch students can adapt.
reranker_ab_test:
retrieval_top_k: 20
variants:
- no_reranker
- small_reranker_top_5
- stronger_reranker_top_5
measure:
- evidence_accuracy
- answer_quality
- added_latencyKey terms in this lesson
The big idea: second look. A local model app is not done when the model answers once; it is done when the whole workflow can be installed, measured, trusted, and recovered.
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “Reranker Evals: The Second Look at Evidence”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 18 min
Embedding Evals: Measure Retrieval Before the Chat Model
Students should test whether embeddings find the right evidence before judging the final answer.
Creators · 19 min
Latency Benchmarks: TTFT, Tokens per Second, and User Feel
A local model that is technically capable can still feel bad if time-to-first-token or generation speed is too slow.
Creators · 8 min
ChatGPT Memory: When To Enable, When To Turn It Off
Memory is supposed to make ChatGPT feel personal. It also quietly accumulates context that can pollute later conversations or leak into the wrong workspace.