Advanced AI Evaluation Techniques to Improve Product Performance
Discover how to identify and fix hidden AI failures in your product with systematic evaluation methods.
1Growing demand for eval skills
Nearly half of 25 top PM job openings recently listed required experience with AI evaluation systems. Industry leaders like Anthropic's former CPO and Y Combinator's CEO emphasize evals as critical for AI product success. Companies using evals report significant improvements, including Shopify's 2.2x faster workflow builder and Ramp's 48% accuracy boost in receipt collection.
2The hidden cost of skipping discovery
Most teams jump straight to writing metrics without proper error discovery, leading to measuring wrong aspects. This approach risks building dashboards around generic metrics that misguide product development. Error discovery, like product discovery, reveals which AI failures truly matter and should be prioritized for measurement.
3Criteria drift challenge
Automated tools often miss failures requiring product-specific judgment, like a leasing assistant failing to suggest cheaper alternatives. This criteria drift occurs when reviewing examples changes your definition of 'good'. Human review remains essential to identify context-dependent failures that automated systems overlook.
4Automation-human balance
While coding agents can process data faster than humans, they're less reliable for context-dependent evaluations. A study of 100 leasing assistant traces showed agents missed 35% of issues requiring product judgment. The solution combines human oversight with agent automation through active learning methods.
5Trace collection essentials
Effective error discovery requires comprehensive traces including user input, system prompts, intermediate steps, and final output. For pre-launch products, synthetic data can help, though real user data is preferable. The process involves defining key failure dimensions and generating diverse test scenarios.
6Error discovery workflow
The recommended process starts with a diverse sample of traces, identifies failures through human review, then scales with agent assistance. A specialized evals plugin guides this workflow, automatically creating customized review interfaces. The entire process takes about 30 minutes once learned.
7Practical implementation steps
First instrument your app to log complete session traces. Then use the evals-start skill to analyze them. The system creates a custom review interface for your data schema. This approach has helped uncover major product flaws across 50+ companies.
8Synthetic data generation
When real user data isn't available, define key dimensions of potential failures and generate test scenarios. For a leasing assistant, this might include task types, renter profiles, and request clarity. Each scenario combination produces a distinct test case for evaluation.
9Active learning approach
The method involves reviewing a diverse sample, identifying failure patterns, then having agents annotate similar cases. This balances automation efficiency with human judgment. Coding agents excel at managing this sampling and annotation process at scale.
10Real-world impact
Companies implementing these methods report tangible results: Cursor achieved 41% cost reduction, Harvey doubled quality scores, and ElevenLabs improved system reliability. These outcomes demonstrate how systematic evals create compounding product advantages.
11Evolving best practices
The field has shifted from 'error analysis' to 'error discovery', emphasizing identifying what to measure rather than just analyzing known issues. This reflects growing recognition that defining the right metrics is more valuable than perfecting wrong ones.
12Strategic advantage
As AI changes faster than human review capacity, evals become essential guardrails. They turn subjective quality judgments into reproducible tests, creating compounding improvements. Teams that master this skill gain significant competitive edge in AI product development.