Some words are constraints
Natural-language questions contain both intent and precise information. In a query about a connector for a specific model, the desire for a replacement is broad intent; the model identifier is a constraint. Losing the distinction can produce a plausible but unusable result.
List the query elements that must influence selection. Versions, account identifiers, part numbers, and legal names often deserve different treatment from the surrounding descriptive language.
Keep useful fields separate
Do not rely on a long description to carry every important identifier. Preserve structured source fields so the application can apply supported filters or explicit business rules where needed.
The searchable description should still explain the thing well. Clear source content helps semantic retrieval, while structured identity and constraints help the application decide which candidates are eligible.
Combine text and meaning deliberately
Text signals can help retain important terms when semantic search retrieves related meaning. Polygres AI Search includes text-hybrid and composition paths in its collection-based workflow.
Choose a combination based on actual examples. If an exact term should be mandatory, do not assume a ranking preference enforces that requirement. Treat eligibility and relevance as separate concerns in the application design.
Evaluate the failure you care about
Create test questions where similar records differ in one important attribute: a version, a connector, or a product code. Check whether the retrieved context contains the right record, not simply a related description.
Keep ambiguous queries in the evaluation set too. Sometimes the correct application behavior is to ask for a missing requirement rather than silently choosing one of several plausible interpretations.
Explore the next step
Continue with the platform guide. For the current setup and API contract, use the Polygres documentation.