AI and WorkflowsIntermediate
Evaluating AI Output Quality With a Scoring Pipeline
Build a lightweight evaluation loop that scores AI responses against a golden dataset so you can detect regressions before users notice them.
Tutorial overview
Preview the structure, then sign in or continue in the secure learning workspace.
1
Building the Golden Dataset2 steps
2
Automated Scoring Methods2 steps
3
Regression Detection2 steps
Related tutorials
More structured learning in AI and Workflows.
AI and WorkflowsIntermediate
Automating Content Moderation With an AI Classification StepBuild an AI classification pipeline that flags submitted content for human review, reducing moderator workload without removing human oversight.
AI and WorkflowsIntermediate
Building Semantic Search That Understands Intent Not Just KeywordsCombine vector similarity search with keyword filtering to build a search experience that finds relevant results even when exact words do not match.
AI and WorkflowsIntermediate
How to Implement a Per-User AI Token Budget to Control CostsTrack monthly token usage per user in your database and enforce soft and hard limits so your AI feature scales without runaway API costs.
AI and WorkflowsIntermediate
How to Use Function Calling to Make AI Return Structured JSON ReliablyUse the OpenAI or Claude tools parameter to guarantee structured JSON output so you can parse AI responses in code without fragile string matching.