7 Habits of Highly Effective Generative AI Evaluations - Justin Muller

Key Concepts

Generative AI evaluations, scaling GenAI workloads, evaluation frameworks, problem discovery, measuring quality, science projects vs. successful projects, prompt decomposition, semantic routing, fast evaluations, quantifiable evaluations, explainable evaluations, segmented evaluations, diverse evaluations, traditional evaluation methods, gold standard sets.

1. The Central Challenge: Scaling Generative AI and the Role of Evaluations

• Main Point: The biggest challenge in scaling generative AI workloads is the lack of comprehensive evaluations.
• Details: While concerns like cost, hallucinations, and accuracy exist, the absence of robust evaluation frameworks is the primary obstacle to scaling. Evaluations are the "missing piece" that unlocks the ability to scale GenAI applications.
• Customer Example: A document processing project with six to eight engineers and a 22% accuracy rate was on the verge of being cut. Implementing an evaluation framework revealed the specific problems, leading to trivial fixes and an increase in accuracy to 92% within six months. This project became the largest document processing workload on AWS in North America.
• Key Argument: Evaluations are not just about measuring quality; their primary goal is to discover problems and suggest solutions.
• Quote: "The number one thing that I see across all workloads is a lack of evaluations... it's the missing piece to scaling GenAI." - Justin Mohler

2. Evaluations: More Than Just Measuring Quality

• Main Point: The purpose of GenAI evaluations is to discover problems and suggest solutions, not just to measure quality.
• Details: Traditional AIML evaluations focus on metrics like F1 score, precision, and recall. While these are important, GenAI evaluations should prioritize identifying errors and providing insights for improvement.
• Science Project vs. Successful Project: Evaluations are the key differentiator between a science project and a successful, scalable project. Teams that prioritize evaluations are more likely to achieve significant ROI.
• Example: When asked to spend time on building an evaluation framework, successful teams readily agree and even suggest dedicating more time, while teams treating it as a science project are reluctant.
• Key Argument: A mindset focused on finding errors leads to a more effective evaluation framework design.

3. Addressing the Unique Complexities of GenAI Evaluations

• Main Point: Evaluating GenAI outputs requires considering the reasoning behind the output, not just the output itself.
• Details: Unlike traditional evaluations with specific numerical answers, GenAI often produces free text, which can be evaluated similarly to how essays are graded.
• Example: The 2x4 Analogy: A drilled hole might appear successful, but the methodology used to create it (e.g., using a dangerous or inefficient method) can reveal underlying problems.
• Example: The Meteorology Company: A weather summary that incorrectly states "sunny and bright" despite sensor data indicating rain might receive a low score. However, understanding the model's reasoning (e.g., prioritizing mental health) provides valuable insights for fixing the problem.
• Key Argument: Evaluating the reasoning behind the output is crucial for identifying and addressing underlying issues in GenAI models.

4. Prompt Decomposition: Breaking Down Complex Prompts for Effective Evaluation

• Main Point: Prompt decomposition involves breaking down large, complex prompts into a series of smaller, chained prompts to enable more granular evaluation.
• Details: This technique allows for attaching evaluations to each section of the prompt, identifying specific areas of strength and weakness.
• Example: The Weather Company (Prompt Decomposition): The weather company's prompt included instructions for determining wind speed. By decomposing the prompt, they discovered that the model was incorrectly comparing wind speed values. Replacing the GenAI component with a Python mathematical comparison resulted in 100% accuracy.
• Impact on Evaluations: Prompt decomposition allows for targeted evaluation, identifying whether GenAI is even the right tool for a specific task.
• Semantic Routing: A common pattern involving semantic routing (e.g., directing easy tasks to small models and hard tasks to large models) benefits from prompt decomposition and individual evaluations for each step.
• Key Argument: Prompt decomposition improves accuracy by removing unnecessary instructions ("dead space") and enabling the use of the most appropriate tool for each task.

5. Seven Habits of Highly Effective Generative AI Evaluations

• Main Point: These seven habits are common trends observed in successful, scaled GenAI workloads.
  1. Fast: Evaluations should be fast (target: 30 seconds) to enable rapid iteration and improvement. This involves parallel processing for generation and judging, followed by a summary of results.
  2. Quantifiable: Evaluations should produce numerical scores to track progress and compare different approaches. Averaging across numerous test cases mitigates jitter in individual scores.
  3. Explainable: Evaluations should provide insights into the reasoning behind the model's output and the judge's scoring. This helps identify the root causes of errors and improve prompt engineering.
  4. Segmented: Complex workloads should be broken down into multiple steps, with each step evaluated individually. This allows for identifying the most appropriate model for each task.
  5. Diverse: Evaluations should cover all relevant use cases, including edge cases, to ensure comprehensive testing.
  6. Traditional: Traditional evaluation methods (e.g., numeric comparisons, database accuracy evaluations, cost and latency measurements) should be used where appropriate.
  7. Gold Standard Set: The gold standard set is the most important part of the evaluation process. It should be created by humans and not by GenAI.

6. Visual Example of an Evaluation Framework

• Process:
  1. Start with a gold standard set of inputs and expected outputs.
  2. Input a test case into the prompt template and LLM to generate an output.
  3. Compare the generated output with the corresponding answer from the gold standard set using a judge prompt.
  4. The judge generates a score and reasoning.
  5. Categorize the results (based on categories defined in the gold standard set).
  6. Generate a summary of the right and wrong answers for each category.

7. Synthesis/Conclusion

Effective generative AI evaluations are crucial for scaling GenAI workloads. They go beyond simply measuring quality and focus on discovering problems, suggesting solutions, and providing insights into the model's reasoning. By adopting the seven habits of highly effective evaluations – fast, quantifiable, explainable, segmented, diverse, traditional, and gold standard set – teams can build robust evaluation frameworks that enable rapid iteration, targeted improvements, and ultimately, successful GenAI deployments. The gold standard set should be created by humans and not by GenAI.