How an AI agent can convert search system context into a quality investigation brief with evidence, diagnostics, and recommended fixes
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant Turbopuffer context, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent convert search system context into a quality investigation brief with evidence, diagnostics, and recommended fixes?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
What this agent helps you do
A Turbopuffer search quality agent helps teams understand why retrieval is not meeting expectations. It can summarize schema, ingestion, query, and result quality questions.
When to use this workflow
Use it when search results are irrelevant, recall drops, ingestion fails, latency changes, or a new retrieval feature needs validation.
How Turbopuffer gives the agent context
Connect the plugin and scope the agent to the namespace, schema, query examples, documents, or incident. Ask it to begin with safe diagnostics and prepare changes for approval.
Example starter prompt
Investigate this Turbopuffer search quality issue. Review the provided query examples, schema assumptions, ingestion context, likely causes, and recommend diagnostics and fixes without making changes automatically.Suggested workflow steps
Define the symptom, gather examples, inspect schema and ingestion assumptions, compare expected and actual results, and rank likely fixes by impact and risk.
Keep the Turbopuffer namespace, exact query, retrieval mode, filters, requested fields, result identifiers, scores where available, and expected relevant documents in each test case. Separate missing documents from poor ranking.
Build a small accepted evaluation set before changing schema, embeddings, full-text configuration, filters, or ranking logic. Compare one change at a time against relevance and latency, and keep production writes or namespace changes behind approval.
Questions this workflow answers
Why are relevant documents missing or ranked below weak results for these searches?
The agent turns reported failures into test cases with namespace, exact query, retrieval mode, filters, requested fields, expected relevant documents, returned identifiers, and available scores. It distinguishes recall failures, where expected documents never appear, from ranking failures, where they appear too low. That distinction changes whether ingestion, filters, representation, or scoring deserves attention.
For each case, it checks document presence, metadata, vector or text fields, filter behavior, query transformation, and result ordering. A missing document may have the wrong tenant or status metadata rather than a poor embedding. A weak ranking can come from ambiguous relevance labels, so an owner must accept the expected set before tuning.
Changes are evaluated one at a time against a small fixed suite containing successful and failed queries. The agent reports relevance movement, latency, and regressions elsewhere. Schema changes, reingestion, embedding replacement, and production namespace writes remain approval-based and include rollback or comparison plans.
The quality brief includes cases, source documents, diagnosis, alternative explanations, proposed tests, and acceptance measures. Search owners review relevance judgments; engineers review operational risk. The goal is to explain and measure the failure before changing the retrieval system.
Expected handoff
The handoff should include examples, findings, diagnostic steps, recommended changes, and validation checks. Pair with GitHub or PostHog for code and product context.
Start with a labeled query set that names expected documents and explains why they are relevant. The agent can inspect whether those records exist in the correct namespace, their text and metadata survived ingestion, filters exclude them, and their scores place them below weaker results. It should compare failing queries with nearby passing ones and keep retrieval, filtering, freshness, and content problems separate. A recommendation might change chunking, metadata, filter logic, or ranking, but each needs an offline before-and-after result and a regression set. Improving one query by hard-coding its vocabulary is not a quality fix if neighboring intents get worse.