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LLM Evals: Common Mistakes

3 min read

Obsidian Metadata

channelHamel Husain
urlhttps://www.youtube.com/watch?v=GL0XhAj5LPE
published2025-05-07

Summary

Shreya Shankar and Hamel Husain discuss common pitfalls in creating effective domain-specific LLM evaluations. They emphasize that generic foundation model benchmarks are inadequate for application-specific needs, and outsourcing labeling or prompting to non-experts leads to poor results. Key recommendations include building custom data annotation tools, crafting specific LLM prompts based on thorough error analysis, utilizing binary labels for clarity, diligently reviewing raw data, being cautious of overfitting to test sets, and conducting online tests for real-world validation.

Key Takeaways

  • Foundation model benchmarks are not suitable for application-specific evaluations.
  • Generic evaluations provide little practical value.
  • Labeling and prompting for evals should not be outsourced to non-domain experts.
  • Develop your own data annotation application tailored to your needs.
  • LLM prompts for evaluation should be specific and grounded in error analysis.
  • Employ binary labels for clearer evaluation outcomes.
  • Thoroughly inspect and understand your data.
  • Guard against overfitting to your test data.
  • Implement online tests for real-world performance validation.

Mindmap

graph TD
    A[LLM Evals: Common Mistakes] --> B{Evaluation Strategy}
    A --> C{Data & Labeling}
    A --> D{Prompting & Analysis}
    A --> E{Testing & Validation}

    B --> B1[Avoid Generic Benchmarks]
    B --> B2[Generic Evals Are Useless]

    C --> C1[Don't Outsource Labeling & Prompting]
    C --> C2[Make Your Own Data Annotation App]
    C --> C3[Look at Your Data]
    C --> C4[Use Binary Labels]

    D --> D1[LLM Prompts Specific & Grounded in Error Analysis]

    E --> E1[Be Careful of Overfitting to Test Data]
    E --> E2[Do Online Tests]

Notable Quotes

Graph View