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This video is a tutorial with the intent to educate product builders on using Stacks, an AI evaluation platform. The speaker highlights how traditional AI prompt testing is subjective and manual, but Stacks enables data-driven, repeatable evaluations. By using custom and pre-built evaluators, users can benchmark, analyze, and iterate on AI models (like travel recommendation agents) based on hard data—not gut feelings—resulting in more reliable AI product development.
- Manual prompt testing is subjective and time-consuming
- Stacks enables creation of evaluation projects to codify desired AI behavior
- Data sets can be built manually or imported from production (CSV upload)
- Supports any major model provider or custom API-connected models
- Outputs can be reviewed with human ratings for quality
- Evaluators (automated scoring systems) check if outputs meet criteria
- Prebuilt evaluators cover basics (e.g., instruction-following)
- Custom evaluators let you measure unique traits (e.g., finding “hidden gems”)
- Evaluators can be run at scale for batch analysis
- Evaluator scores show strengths and weaknesses for each output
- Aggregated metrics enable head-to-head comparison of models and prompts
- Results inform real product decisions—speed vs. quality, etc.
- Instant re-runs accelerate testing new models, prompts, or agent flows
- Stacks turns subjective AI evaluation into repeatable, actionable measurement
- Product builders gain confidence in choosing the right models and prompts for their use case