From: aidotengineer
The largest challenge in scaling generative AI applications is evaluations, which are frequently the missing piece in a project’s ability to scale [01:21:00]. While concerns like cost, hallucinations, accuracy, and capacity are often raised, a lack of robust evaluations is the primary impediment [01:43:00]. For example, one document processing workload, initially achieving 22% accuracy, was able to reach 92% accuracy and become the largest of its kind on AWS in North America after an evaluation framework was implemented [02:20:00].
Beyond Traditional Metrics: The Goal of Generative AI Evaluations
In traditional AI/ML, evaluations often focus on measuring quality using metrics like F1 score, precision, and recall [04:15:00]. While generative AI evaluations also produce scores, their primary objective is to discover problems and suggest solutions [04:43:00]. The mindset when designing a generative AI evaluation framework should be to find errors, which leads to a different design approach than simply measuring performance [05:08:00].
Unique Complexities in Generative AI Evaluation
Evaluating AI Agent Evaluation for generative AI presents unique challenges compared to traditional machine learning:
- Free Text Output: Unlike traditional models that might output specific numbers or classifications, generative AI produces free-form text [07:02:00]. While humans have been grading free text for centuries, it makes mathematically exact calculations difficult [07:14:00].
- Evaluating Reasoning, Not Just Output: The methodology or reasoning behind a generative AI’s output is as important as the output itself [09:22:00]. A correct answer achieved through flawed reasoning indicates a systemic problem that needs to be addressed for future reliability [09:36:00].
- Example: A weather summary model correctly states it’s sunny, but its internal reasoning reveals it ignored rain data because “it’s important to mental health to be happy” [10:18:00]. While the output is correct in one scenario, the underlying logic is flawed and needs fixing. Asking the model to explain its reasoning provides crucial insight [10:30:00].
Prompt Decomposition and Segmentation
Large, complex prompts are challenging to evaluate effectively with a single end-to-end metric, much like trying to find an electrical fault in a complex circuit with a single multimeter reading [11:51:00]. Prompt decomposition involves breaking down a large prompt into a series of chained, smaller prompts [13:02:00].
- Benefits for Evaluations:
- Localized Error Identification: Allows attaching an evaluation to each section of the prompt, pinpointing where errors occur [13:09:00].
- Optimized Tool Selection: Helps determine if generative AI is the right tool for a specific sub-task [13:18:00]. For instance, a mathematical comparison (e.g., “is 7 larger than 5?”) is better handled by Python than by a large language model [13:30:00].
- Increased Accuracy and Efficiency: By breaking down tasks and using the most appropriate tool (including traditional programming) for each step, accuracy significantly increases, and costs decrease by removing “dead space” or unnecessary tokens from prompts [14:50:00].
- Semantic Routing: A common pattern where an initial step classifies an input and routes it to an appropriate model (e.g., small model for easy tasks, large model for hard tasks) [14:03:00]. Evaluating each routing step independently is crucial, often using traditional numeric evaluations for the routing decision itself [14:37:00].
Habits of Effective Generative AI Evaluations
Successfully scaled generative AI workloads typically incorporate the following evaluation habits:
- Fast: Evaluations should run quickly (e.g., target 30 seconds for a full framework run) to enable rapid iteration and hundreds of changes daily [15:50:00]. This contrasts with slow, manual testing that limits development pace [16:09:00].
- Quantifiable: Even with free-text outputs, evaluations should produce numbers [18:21:00]. To account for potential “jitter” in scores (due to the probabilistic nature of LLMs), use numerous test cases and average results [18:54:00].
- Explainable: Evaluations should provide insight into why the model produced a particular output and how the judge reasoned its score [20:09:00]. This is akin to a professor providing a rubric and feedback, not just a grade [20:50:00].
- Segmented: For multi-step workloads, evaluate each step individually [21:24:00]. This allows for choosing the most appropriate (and often smallest, cheapest) model for each specific task, improving efficiency and accuracy [21:47:00].
- Diverse: Cover all relevant use cases with a broad set of test cases, including edge cases and scenarios where the model should not respond [22:10:00].
- Traditional: Do not abandon traditional AI/ML techniques. Numeric evaluations, database accuracy metrics (like F1 score for retrieval), cost, and latency measurements are still vital components of a comprehensive evaluation framework for generative AI [22:30:00]. It’s not a replacement, but an integration.
Evaluation Framework Setup
An effective evaluation framework typically starts with a gold standard set of inputs and desired outputs [23:31:00]. This set is crucial; errors in the gold standard will propagate throughout the evaluation process [23:42:00]. Generative AI should not be used to create the gold standard set, as it might introduce the same errors the system aims to fix [24:00:00].
The process involves:
- Taking an input from the gold standard set [24:26:00].
- Feeding it into the prompt template and LLM to generate an output, which includes both the answer and its reasoning [24:28:00].
- Comparing the generated output with the matching gold standard answer using a “judge prompt” [24:38:00].
- The judge generates a score and the reasoning behind that score [24:45:00].
- Categorizing results (e.g., based on input categories in the gold standard) to summarize right and wrong answers, providing actionable insights for improvement [24:54:00].