AI and Workflows

Build an AI classification pipeline that flags submitted content for human review, reducing moderator workload without removing human oversight.

Combine vector similarity search with keyword filtering to build a search experience that finds relevant results even when exact words do not match.

Build a lightweight evaluation loop that scores AI responses against a golden dataset so you can detect regressions before users notice them.

Track monthly token usage per user in your database and enforce soft and hard limits so your AI feature scales without runaway API costs.

Use the OpenAI or Claude tools parameter to guarantee structured JSON output so you can parse AI responses in code without fragile string matching.

Implement exponential backoff, per-task retry logic, and user-facing error states so your AI features degrade gracefully instead of silently failing.