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7 Habits of Highly Effective Generative AI Evaluations (AI column)

#AI

I saw something interesting this weekend. speech About evaluations Justin Mueller.Principal Applied AI Architect at Amazon Web Services (AWS). Mueller’s team at AWS is helping clients scale workloads with generative AI, and during his tenure, he has had the opportunity to study more. 100 various attempts to create frameworks for assessing the work of models. This presentation was held at the AI Engineer World’s Fair. The key idea of the speech was that the main problem when scaling genAI solutions is the lack of evaluations. (evaluations) the quality of their work. Despite what many cite as the main issues of cost, hallucinations, accuracy, and performance, it is the lack of suitable evaluation frameworks that most often hinder successful implementation. To demonstrate this idea, Justin told a real client case with a document processing project, where the accuracy was everything. 22%. After the introduction of the evaluation system, 6 months have passed 92The project went into mass production, making it the largest AWS document processing facility in North America. To solve this problem with the effective evaluation of models, the author suggests the following seven habits: ~~high-performing~~ Highly effective genAI evaluations:

1. Speed of execution (Fast)

  • Target time for evaluation - 30 seconds The ability to make hundreds of changes and tests daily instead 4-8 monthly 2. quantitatively measurable (Quantifiable) Evaluation should be expressed in specific numerical indicators It is worth applying averaging over multiple test cases to eliminate random fluctuations 3. Explanation (Explainable) Analysis not only of the results, but also of the reasoning process of the model The importance of understanding the logic of both the generating model and the evaluating model 4. Segmentation (Segmented) Breaking complex prompts into a sequence of simple steps The ability to evaluate each stage separately and select a suitable model for each task 5. Diversity (Diverse) Coverage of all use cases
  • The rule of thumb:100 Test examples for major use cases 6. Traditional (Traditional)
  • Maintaining the use of proven evaluation methods (ML) Numerical estimates, database accuracy metrics, cost measurement and delays remain relevant 7. Gold standards The critical importance of creating a quality set of gold standards Avoid using generative AI to create gold standards to avoid replicating errors

The key principles of Justin are the following: - Decomposition of prompts Breaking complex multi-step prompts into a chain of simple ones, which allows you to accurately determine the source of errors and optimize each stage separately. - Semantic routing Intelligently directing queries to suitable models depending on the complexity of the task, which increases accuracy and reduces costs. - Focus on identifying problems The primary purpose of evaluations is not simply to measure quality, but to identify specific problems and propose solutions.

In general, Mueller’s speeches are a collection of practical tips on how to organize your GenAI model evaluation framework. These tips sound logical, look affordable, and are also tested from experience with the largest workloads in North America.