Final artifact
A retrieval review report, a completed evidence-chain checklist, and a real set of retrieval failure cases.
Assessment
Retrieval and grounding audit: make the evidence chain explicit
This lesson forces you to audit one real retrieval workflow into an evidence chain instead of saying 'we already have a knowledge base'. The deliverable is a full query-to-citation report, source and freshness judgment, and a set of retrieval failure cases.
Real acceptance criteria
Not that retrieval appears to work, but that you can explain the job and failure mode of query, filtering, injection, citation, and freshness.
Where our value shows
This page turns evidence-routing order, the retrieval ladder, noise recognition, and templates into a reusable runbook.
DepthPilot Summary
This lesson teaches how to turn RAG into an evidence chain instead of document stuffing
Many systems claim to be grounded while really just stuffing documents into context. DepthPilot focuses on connecting query, filter, citation, freshness, and failure classification into one evidence chain. After this lesson, the user should be able to explain whether a bad answer failed because evidence was missing, noisy, or stale.
Decide when evidence is required
- Not every question needs retrieval, but evidence-bound questions must be marked explicitly.
- The first step of grounding is not adding a knowledge base. It is deciding which answers cannot rely on model memory alone.
- If you do not know when evidence is required, citation and freshness design will float.
Control how evidence enters context
- Query, filter, top-k, and rerank jointly determine whether context contains signal or noise.
- Citation is not decoration. It is the hook that lets the system trace evidence back.
- Weakly relevant long text is often more dangerous than no retrieval because it dilutes strong evidence.
Make failures replayable
- Retrieval failure, rerank failure, and answer-synthesis failure should be logged separately.
- Freshness gaps and dirty sources should become explicit failure labels.
- The real deliverable is an auditable evidence chain, not a system that merely has a knowledge base.
Evidence routing order
Define which questions must retrieve evidence and which can answer directly.
Design query and filters before you decide how chunks enter context.
Design citations, source metadata, and freshness together instead of only retrieving text.
Define whether the system should clarify, downgrade, or refuse when retrieval quality is poor.
Retrieval ladder
Check whether the query reflects the real user need before tuning top-k.
Inspect whether results are relevant but useless, or irrelevant yet scored highly.
Separate retrieval failure, rerank failure, context injection failure, and answer-synthesis failure.
Keep failure cases as future eval material instead of treating them as one-time debugging noise.
High-signal bad patterns
Treating the mere existence of a knowledge base as proof of grounding.
Retrieving chunks without visible citations, source metadata, or time information.
Injecting too much weakly relevant text so the real evidence gets diluted.
Answering time-sensitive questions without any freshness policy.
Proof you must keep before launch
One evidence path from query rewrite to final citation.
One source and freshness policy explaining what can be trusted and how long it stays trustworthy.
One set of retrieval failure cases showing false hits, missed hits, or noisy hits.
One short recap of whether the workflow is most threatened by missing evidence, dirty evidence, or stale evidence.
Reusable retrieval templates
Download the retrieval review report
Capture query, filters, citations, freshness, and failures in one review artifact.
Download the evidence chain checklist
Use it to test whether grounding is real or just knowledge-base theater.
Search Cluster
Connect retrieval audits back to discoverable evidence paths
High-intent users often enter through retrieval, grounding, observability, or eval-checklist searches before they commit to a real evidence-chain review.
Retrieval and Grounding Guide
A retrieval and grounding guide that goes beyond dumping documents into RAG
Many users search for retrieval or grounding because they want to feed documents into a model. DepthPilot focuses on something stricter: when evidence is required, how it is filtered, and how source traceability stays visible in the final answer.
Open pathLLM Observability Guide
An LLM observability guide focused on replayable failures, not just more logs
Many users search for LLM observability because the system broke and they do not know how to inspect it. DepthPilot focuses on something stricter: recording traces, labeling failures, and replaying bad runs so debugging becomes systematic.
Open pathAI Eval Checklist
An AI eval checklist for deciding whether the system actually improved
Users searching for an AI eval checklist usually do not lack opinions. They lack an executable review frame. This page condenses the minimum eval logic into a checklist-style entry point.
Open pathReference appendix
These links anchor the method. The actual lesson is the evidence-routing order, retrieval ladder, bad-pattern recognition, and templates above.