Keep the successful example, then challenge it
A prototype often begins with a question that works well. Preserve that example, but add the cases that stress its assumptions: an exact identifier, a missing record, an outdated source, and a caller with a different access scope.
The goal is to understand where the approach works and where the application needs a different response. An unsupported answer should not be treated as success just because it is delivered fluently.
Make data preparation repeatable
Record how source rows become searchable points and graph relationships. Keep stable identifiers, source references, and the update process with that workflow.
Use the CLI and documented project operations where they fit repeatable setup. Keep application retrieval in the Python SDK or Runtime API path you intend to operate, rather than relying on manual dashboard steps that no one can reproduce later.
Give the application clear states
A project can be connected while still preparing its data. A query can return no relevant evidence. A source can lack a compatibility fact. Decide how the application represents these states instead of collapsing them into a generic answer.
For synchronized projects, keep the source write path explicit and check readiness before routing normal query traffic. Preserve errors and source identifiers so a failure can be diagnosed at the right stage.
Review the whole workflow
Measure retrieval quality with expected evidence, review the caller’s access boundary, and estimate the capacity of the actual representation. Check latency under the conditions you intend to support.
Then connect answer generation and inspect whether it uses the retrieved evidence accurately. Polygres supplies the retrieval foundation; a dependable application comes from making the surrounding decisions visible and testable.
Explore the next step
Continue with the platform guide. For the current setup and API contract, use the Polygres documentation.