1 link tagged with all of: context engineering + evals + retrieval augmented generation + dbt
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Claire Gouze lays out a practical process to build and test a context layer your data agents can trust. She shows how to start small with dbt docs and markdown files, write evals, clean up data models, and iterate until you reach around 90% reliability.
- Adding query logs and profiling alone got the agent stuck around 40% accuracy; reworking the data model and documentation pushed it to 90%.
- Treat context like code: store it in files/markdown, version in Git, add CI/CD evals, and iterate on failure cases rather than plugging agents directly into raw databases.
- Start small—10 to 20 core tables with existing dbt docs—rather than trying to cover the whole warehouse at once.
- Evals should come from real BI query history or autogenerated question sets, reviewed by teams and tracked over time to build trust in the agent.