SUMM

Evals are a key component in AI application development, necessary for crafting and improving systems.
Challenges in AI include testing applications with nondeterministic outputs and subjective judgments.
Evals provide the required tools and metrics to measure AI model performance and accuracy.
Measurement is crucial for business impact, aligning applications with goals, continuous improvement, trust, and accountability.

The process starts with sourcing data, often beginning small with synthetic or artificial data.
Evaluation is a continuous improvement process, requiring ongoing observation and refinement of data sets.
Proper data labeling is essential to cover multiple application flows and aspects.
Multiple, specialized data sets are recommended for comprehensive coverage of different flows or goals.

Clearly define evaluation goals and objectives for each system component.
Modularize components and optimize data handling for distinct flows.
Each application flow or output path must be evaluated systematically.
There is no universal eval; methods must be adapted to the specific application type (e.g., RAG, code generation, Q&A).
Evaluation targets can differ: accuracy, similarity, usefulness, functional correctness, robustness, etc.
For agents, trajectory and multi-turn simulation evaluation are necessary to capture complexity and conversation paths.
Tool use cases require checks for correctness and thorough test suites.

Employ orchestration and parallelism to manage and run evals at scale, with frequent and regular execution.
Aggregate intermediate and regression results for broader analysis.
Measurement should follow a cyclical process: measure, monitor, analyze, and repeat.
Strategy selection depends on use case; there is no fixed eval method.
Trade-offs exist between human-in-the-loop fidelity and automated speed; a balanced approach is suggested.
The evaluation process should take precedence over reliance on specific tools due to automation limits.

Evals are fundamental to robust AI application development.
The "eval development" paradigm emerges, akin to test-driven development, but focused on crafting evals for specific use cases.
Both positive and negative scenarios must be included in evaluations.
Data quality and coverage remain the central priority.
Teams must continuously monitor, analyze, and iterate on eval results for ongoing improvement.
Balancing fidelity and speed—and adapting methodology to needs—is critical.

How to run Evals at Scale: Thinking beyond Accuracy or Similarity — Muktesh Mishra, Adobe