Building and Evaluating AI Products

Key Points and Main Ideas

• Developing AI products often involves more time evaluating and iterating the model rather than programming.
• Real-world cases are unpredictable, requiring constant monitoring and iteration.
• Human evaluation is essential but becomes impractical at scale.
• Automated evaluation systems using large language models (LLMs) can address this issue.

Challenges in AI Development

• AI models often fail or give unexpected results, particularly in dynamic, real-world environments.
• Need to handle diverse and complex customer requests not anticipated during initial development.
• Iteration is time-consuming due to numerous combinations of settings, prompts, and models.
• Lack of confidence in system improvements due to non-deterministic nature of LLMs.

Importance of Evaluation

• Evaluating AI performance is crucial to measure improvement and find optimal settings.
• Existing public evaluations may not align with specific tasks, making custom evaluations necessary.
• Commonly used evaluation methods include human review and automated evaluations using LLMs or encoded rules.

Steps to Build an Evaluation System

1. Choose Relevant Metrics

   • Metrics should address the most frequent failure points of the system.
   • Examples of metrics:
     • Contextual relevance
     • Faithfulness (non-hallucination)
     • Latency
     • Task completion accuracy

2. Build the Evaluator

   • Decide on input data and outcomes for evaluation.
   • Use prompt templates for evaluation.
   • Examples of evaluator designs:
     • Relevance classification (relevant vs. irrelevant)
     • Instruction following and task completion in agents

3. Prepare a Golden Data Set

   • Collect a set of test cases with known correct outcomes.
   • Methods to build this data set:
     • Manually curate small sets
     • Collect user logs from production and annotate
     • Generate synthetic data using large models

4. Test and Compare Results

   • Evaluate system variations and compare performance against chosen metrics.
   • Use evaluation results to iterate and optimize.
   • Automated logging systems like LMs (LangSmith) can help track and analyze these variations.

Example: Building a Web Research Agent Evaluator

• Logging System Setup: Use LangSmith to automatically track functions and model calls.
• Metrics Definition: Focus on info gathering ability.
• Evaluator Creation: Use prompt templates to check if the agent gathered necessary information.
• Experimentation: Compare performance across different model versions (e.g., GPT-4 vs. GPT-3.5).

Conclusion

• Iterative evaluation is key to improving AI systems' performance and reliability.
• Automated evaluation systems reduce human labor and can process larger volumes of data.
• Building effective evaluation systems ensures better and faster improvements to AI products.

Note: Stay updated with AI knowledge and ensure continuous learning and experimentation.