What can retrieval grading over traces not tell me?

It can’t tell you about the chunk that was sitting in the corpus, was genuinely relevant, and never came back at all, because that chunk leaves no trace of its own absence. A trace only records what the retriever actually returned, so grading traces can catch a chunk that’s irrelevant to the question, an empty result set, or a chunk missing an exception clause the question needed.

Measuring true recall, what fraction of the relevant material actually got retrieved, needs something a trace can’t provide: a labeled set of queries mapped to the document IDs that should have come back for each one. Building that set is exactly the slow, hand-labeled work that grading production traces was meant to avoid.

The two methods answer different questions. Trace grading finds retrieval mistakes you can see. Recall measurement finds the mistakes you can’t, at the cost of the labeling effort you were trying to skip.

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More on this.

Two ways to run Tessary.

Tessary is an open-source agent reliability platform. Cloud and self-hosted run the same workflow on the OpenTelemetry traces your agent already emits.

Tessary Cloud

We host it for you. Send your first trace with nothing to deploy and no model key.

what's includedper organization
traces
10,000 per calendar month
stored trace data
1 GB
retention
30 days
model credit
$10, one-time, for triage and root-cause analysis
credit card
not required

Self-hosted Tessary

Run the open-source code on your own infrastructure with one command. Add your own model key for triage and root-cause analysis.

Self-host Tessary for me by following https://github.com/tessaryai/tessary/blob/main/setup.md

docker compose -f oci://docker.io/tessaryai/tessary:compose up -d -y