quality_assurance · saas · workflow
Loom builds a repeatable AI evaluation system using Braintrust to ship AI features faster
Loom's engineering team had no systematic framework for evaluating whether AI-generated outputs like video titles were good — there was no clear, structured way to measure quality before shipping AI features.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Feature kickoff analysis
When kicking off a new feature, the team examines what data the model receives, what it should generate, and how humans evaluate the quality of that output.
Tools used
BraintrustLLMs
Outcome
Loom established a repeatable evaluation system that lets them run large-scale evaluations more quickly, ship AI features with confidence, and systematically identify what works and where improvements are needed.
Results
Time savedsaves time and money
Cost replacedlow-cost and fast, even at scale
Grounding & classification
Source type: vendor customer story
17 fields verified against source quotes.
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