From: aidotengineer
Traditional evaluation approaches, often rooted in unit and integration testing frameworks from conventional software development, frequently fall short when applied to artificial intelligence (AI) applications [00:01:40]. While many teams believe their existing testing frameworks are robust, a deeper examination often reveals uncertainty about what constitutes an effective evaluation framework for AI [00:02:39].
Fundamentals of Evaluation
To assess quality before deployment, an evaluation requires three key components [00:02:49]:
- Agent: Whatever is being evaluated, ranging from an end-to-end AI agent to a small function or retrieval pipeline [00:02:59]. Each agent, such as a customer service chatbot or a Q&A agent for legal contracts, has unique requirements that traditional evaluations may not capture [00:03:19].
- Dataset: The benchmarks against which the agent is evaluated [00:03:51].
- Evaluators: The methods used to measure quality [00:04:52].
Limitations of Traditional Approaches
Inadequate Datasets and Test Coverage
A significant stumbling block for many teams is the dataset [00:03:56]. Traditional approaches often rely on:
- Handwritten Test Cases: Developers manually create a few test cases, assuming they cover all use cases, which is often not true [00:04:07]. These static test cases tend to focus only on “happy paths” and miss critical edge cases where issues might arise in production [00:04:29].
- Lack of Domain Expertise: Ideal test cases, including both inputs and desired outputs, should be created by domain experts who understand the business context and quality requirements [00:04:40]. Without this, the dataset may not accurately define what “good” responses look like.
- [[challenges_in_current_ai_benchmarking_practices | Data Set Drift]]: Even if an initial dataset is well-crafted, it can quickly become unrepresentative of real-world usage patterns [00:11:19]. Production users often provide context-dependent, messy inputs or combine multiple questions in unexpected ways [00:11:43]. This means that while internal metrics might still look good, they no longer reflect actual performance, similar to training for a marathon on a treadmill without accounting for real-world conditions [00:12:34].
Inefficient Evaluators
Historically, evaluators typically included:
- Human Evaluators: Subject matter experts review outputs, score them, and provide feedback [00:05:05]. While this method can work, it is notably slow and expensive [00:05:07]. For example, evaluating 1,000 test cases with human evaluators could take a full day of work [00:07:02].
- Rule-Based Evaluators: These are effective for objective metrics like response time or latency [00:05:12]. However, they struggle with subjective or nuanced aspects of AI outputs, such as relevance or adherence to specific standards like ROUGE-L, which may not always align with true quality [00:05:17].
Generalizability Over Specificity
Many established evaluation frameworks and libraries (e.g., Ragas, Promptfoo, LangChain) provide built-in evaluation criteria [00:09:01]. While convenient, these are designed for generalizability and do not necessarily measure what is crucial for a unique use case [00:09:16]. This can lead to “criteria drift”, where the evaluator’s notion of quality diverges from the user’s [00:10:41]. For instance, 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 positive internal test results [00:09:58].
Consequences of Static Evaluation
Treating evaluations as static tests, similar to traditional software testing, is a significant trap in AI [00:18:14]. AI systems, particularly those using Large Language Models (LLMs), require dynamic and iterative evaluation processes [00:06:00]. A static approach leads to:
- Meaningless Evaluations: Tests may not accurately reflect real-world performance or user satisfaction [00:00:15].
- Failure to Catch Bugs: Critical issues only emerge in production, causing user complaints [00:09:50].
- Hindered AI System Development: Without proper evaluations, it’s difficult to build AI systems that truly deliver value in the real world beyond being just “fancy demos” [00:01:59].
The emergence of LM evaluators aims to address these limitations by offering faster, cheaper, and more consistent evaluation [00:06:34]. However, they also introduce their own challenges that must be addressed through continuous alignment with real-world usage and domain expert input [00:10:41].