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Continuous Improvement for LLM Applications

Continuous improvement is a crucial aspect of developing and maintaining high-quality LLM applications. This guide will walk you through the process of evaluating and improving your LLM-powered systems over time.
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Evaluation Framework

Before implementing continuous improvement, it’s essential to establish a robust evaluation framework. Follow these steps to create an effective evaluation process:

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Determine the Evaluation Level

Choose the appropriate level for evaluation:
  • LLM call level (similar to unit tests)
  • Agent run level (similar to integration tests)
  • Conversation level

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Define Evaluation Metrics

Identify what aspects of your LLM application you want to measure:
  • Hallucination rate
  • Answer relevancy
  • Application-specific behaviors
  • Response quality
  • Task completion rate

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Select Evaluation Methods

Choose one or more evaluation methods based on your needs:
  • LLM-as-a-Judge: Use another LLM to evaluate outputs
  • Code-based evaluation: Implement programmatic checks
  • Hybrid approach: Combine LLM and code-based evaluations
  • Embedding similarity: Compare vector representations of responses
  • Human review: Incorporate manual evaluation by experts
You can find more information on how to perform evaluations here.

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Improvement Process

Once you have established your evaluation framework, follow these steps to continuously improve your LLM application:

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Pre-production Iteration

  1. Create a dataset with ground truth examples
  2. Implement your evaluation procedure
  3. Iterate on your LLM application (prompts, code, etc.) to improve performance
  4. Build and test the first production-ready version

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Production Monitoring and Evaluation

Now that you have a production system, implement the following strategies to gather data and improve your application in production:

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Product Feedback Loops

  • Implicit feedback: Track user actions (e.g., accepting or rejecting suggestions)
  • Explicit feedback: Implement user rating systems (e.g., thumbs up/down)

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Human Review

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Automated AI Evaluations

  • Implement reference-free evaluations to continuously monitor performance
  • Use metrics like perplexity, coherence, or task-specific scores
You can find more information on how to perform evaluations here.

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Continuous Improvement Cycle

  1. Analyze data from production monitoring
  2. Identify edge cases and areas for improvement
  3. Add new examples to your evaluation dataset
  4. Update prompts, agent code, or model fine-tuning
  5. Run tests to ensure improvements don’t introduce regressions
  6. Deploy the new version to production

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CI/CD Integration

To ensure that there are no regressions, integrate the following test into your CI/CD pipeline:
  1. Pull the most representative dataset
  2. Run the LLM system and the evaluations
  3. Pull the baseline performance metrics with regards to that dataset
  4. Compare results to the baseline metrics using a confidence interval

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Global Performance Monitoring

Track product metrics such as:
  • Conversion rates
  • User retention
  • Task completion rates
  • User satisfaction scores
Use these metrics to assess the overall impact of your LLM application and guide future improvements.

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Conclusion

By implementing a robust continuous improvement process, you can ensure that your LLM application remains effective, relevant, and valuable to your users over time. Regular evaluation, monitoring, and iteration are key to maintaining a high-quality LLM-powered system.