Improving AI evaluation methods

From: aidotengineer

Effective evaluation of AI models is crucial for building systems that deliver real-world value, not just fancy demos [01:57:00]. Many AI evaluations can be meaningless if not designed and maintained correctly [00:15:00].

Fundamentals of AI Evaluation

Just as traditional software requires unit and integration tests before pushing changes to production, AI applications need robust evaluations before deployment [02:09:00]. A robust evaluation framework requires three key components [02:46:00]:

1. Agent: This is whatever is being evaluated, which could be an end-to-end agent, a small function, or a retrieval pipeline [02:55:00]. Different agents (e.g., customer service chatbot, legal Q&A system) have unique requirements that evaluations must account for, such as accuracy, compliance, explainability, or nuance [03:08:00].
2. Data Set: This is the benchmark against which the agent is evaluated [03:48:00]. A comprehensive data set must include both inputs (queries the system will receive in production) and ideal outputs (what good responses should look like) [04:12:00]. Critically, it must cover not only “happy paths” but also tricky edge cases where things might go wrong [04:24:00]. These examples should ideally be written by domain experts who understand the business context and quality requirements [04:32:00].
3. Evaluators: This refers to how quality is measured [04:52:00].
   • Human Evaluators: Traditionally, subject matter experts review outputs, score them, and provide feedback. While effective, this method is slow and expensive [04:57:00].
   • Code-based Evaluators: These are good for objective metrics like response time or latency [05:09:00].
   • LLM Evaluators: These promise to combine the nuance of human reasoning with the speed and scalability of automated systems [05:21:00].

These three components are dynamic and must evolve as the AI agent improves, evaluation criteria become more sophisticated, and new challenges arise [05:57:00].

The Rise and Challenges of LLM Evaluators

LLM evaluators have become popular due to their compelling advantages [06:19:00]:

• Speed: Evaluations that once took 8-10 hours with human evaluators can now be completed in under an hour [06:43:00].
• Cost: Costs can be reduced by as much as 10x compared to traditional human evaluations [07:16:00].
• Consistency: LLM evaluators can achieve over 80% consistency with human judgments, comparable to inter-human agreement [07:41:00]. Research papers and major model providers are increasingly backing this approach [08:09:00].

However, LLM evaluators face significant problems [08:40:00]:

Criteria Drift

Criteria drift occurs when an evaluator’s notion of “good” no longer aligns with the user’s [10:38:00]. Standard evaluation frameworks often use generalizable criteria, which might not capture the unique requirements of a specific use case [09:10:00]. For example, an e-commerce recommendation system’s evaluator might over-index on keyword relevance, missing the broader context of user intent, leading to user complaints despite seemingly good test results [09:21:00]. Evaluation criteria need to evolve over time to balance true positives and false positives, maximizing alignment with human judgments [10:50:00].

Data Set Drift

Data set drift refers to a lack of test coverage, where carefully crafted test cases fail to represent real-world user inputs [11:16:00]. Real users often provide context-dependent, messy, or complex queries that static, hand-written test suites cannot anticipate [11:38:00]. This means metrics might look good on paper, but the system underperforms in production because the test cases don’t reflect reality [12:15:00].

Improving AI Evaluation Methods: Iterative Alignment

The fundamental insight for effective evaluation is that evaluators and data sets must be iteratively aligned, similar to how an LLM application itself is aligned [12:56:00]. This constitutes continuous improvement for AI systems.

Here’s a three-step approach for best practices for AI evaluation:

1. Align Your Evaluators with Domain Experts:

   • Have domain experts regularly grade outputs, not just at setup, but continuously [13:19:00].
   • Encourage experts to critique evaluator results themselves, identifying what the evaluator is missing or overemphasizing [13:30:00].
   • Use these critiques and few-shot examples in your evaluator prompt to ground it with a real-world notion of what is good and bad [13:36:00].
   • Iterate on the evaluator prompt, customizing it beyond templated metrics to measure what is truly meaningful to your application and business context [13:46:00].
   • Start with a small set of examples (e.g., 20) in spreadsheets to quickly gauge alignment between evaluator judgments and domain expert expectations [15:53:00].

2. Keep Your Data Sets Aligned with Real-World User Queries:

   • Treat your test bank as a living, breathing entity [14:09:00].
   • Log instances where the system underperforms in production and automatically (or manually) flow these real-world failures back into your test suite [14:11:00].
   • Continuously add these test cases and their corresponding ground truth labels to improve your test bank over time [16:43:00].

3. Measure and Track Alignment Over Time:

   • Use concrete metrics like F1 score for binary judgments or correlation coefficients for Likert scales [14:31:00].
   • Track how well your evaluator matches human judgment with every iteration [14:41:00]. This provides critical feedback on whether your evaluator is improving or regressing [14:50:00].
   • Set up a simple dashboard to systematically track these alignment scores [17:28:00].

Ultimately, AI evaluations are only as good as their alignment with real-world usage [18:07:00]. It’s essential to avoid static evaluation and instead build iterative feedback loops into the development process [18:13:00]. The goal is continuous improvement, not perfection [18:00:00].