Tech • IA • Crypto
Evals for taste: Hill-climbing a slide-generation agent
5/10TL;DR
Systematic evaluation frameworks are emerging as essential tools for improving AI agents, enabling developers to replace subjective “vibes” with measurable, actionable performance signals.
KEY POINTS
Evals define measurable AI performance
Evaluations, or “evals,” are structured tests designed to measure how well an AI system performs on specific tasks. They encode expectations through defined scenarios and grading logic, allowing developers to assess output quality, identify weaknesses, and track improvements over time. This shifts AI development from intuition-based judgment to quantifiable performance metrics.
Bridging the gap between perception and reality
Without evals, teams often rely on vague user feedback or subjective impressions, making it difficult to diagnose issues or validate improvements. Evals provide a concrete benchmark, enabling developers to distinguish real regressions from noise and verify whether system changes genuinely improve outcomes.
Limits of generic benchmarks
Widely used benchmarks such as SWE-bench, Terminal-bench, and ARC-AGI offer insight into general model capabilities, but they often fail to reflect specific real-world use cases. Developers are increasingly encouraged to build custom evals tailored to their own applications to ensure relevance and accuracy.
Multiple grading approaches with trade-offs
Evals typically rely on three types of graders. Code-based graders are fast, cheap, and deterministic but lack nuance and can be brittle. Model-based graders use rubric-driven reasoning and can handle subjective qualities like coherence or design, though they are more expensive and less consistent. Human graders provide the highest quality feedback but are slow and costly, making them suitable mainly for spot checks.
Case study: slide generation agent
A practical example using a slide-generation AI agent highlights how evals guide improvement. Initial outputs contained issues such as cluttered layouts, small fonts, and unnecessary emojis. By defining metrics like slide count, text density, and visual clarity, developers identified failure modes and iteratively refined the system.
Iterative improvement through feedback loops
Adjustments to prompts—such as specifying typography rules or discouraging excessive decoration—led to visibly improved outputs. This iterative cycle of testing, analyzing eval results, and refining the system demonstrates how evals enable controlled optimization rather than guesswork.
Challenges in evaluation design
Designing effective evals is complex. Poorly calibrated metrics can produce misleading results, such as high scores for low-quality outputs. Model-based judges, in particular, require careful prompt design, clear rubrics, and sometimes multi-judge consensus to improve reliability.
Importance of calibration and evolution
Evals are not static. As systems evolve, evaluation criteria must be updated to remain meaningful. “Eval saturation” can occur when tests no longer provide useful insights, underscoring the need for continuous refinement.
QA loops enhance reliability
Introducing adversarial quality assurance loops—where one agent critiques another’s output—can significantly improve results. This approach forces systems to identify and fix their own errors before completion, mirroring practices in software testing.
Stronger models reduce but do not eliminate the need for evals
More advanced models can produce higher-quality outputs with minimal prompting, demonstrating improved baseline capabilities. However, evals remain critical for identifying subtle issues, validating performance, and ensuring alignment with specific requirements.
CONCLUSION
As AI systems grow more complex, evals are becoming a foundational component of development, enabling systematic improvement, faster iteration, and more reliable deployment of agent-based applications.