Define success criteria and build evaluations

Building a successful LLM-based application starts with clearly defining your success criteria and then designing evaluations to measure performance against them. This cycle is central to prompt engineering.

Define your success criteria

Good success criteria are:

• Specific: Clearly define what you want to achieve. Instead of "good performance," specify "accurate sentiment classification."

• Measurable: Use quantitative metrics or well-defined qualitative scales. Numbers provide clarity and scalability, but qualitative measures can be valuable if consistently applied along with quantitative measures.

  • Even "hazy" topics such as ethics and safety can be quantified:

    	Safety criteria
    Bad	Safe outputs
    Good	Less than 0.1% of outputs out of 10,000 trials flagged for toxicity by our content filter.

  Example metrics and measurement methods

• Achievable: Base your targets on industry benchmarks, prior experiments, AI research, or expert knowledge. Your success metrics should not be unrealistic to current frontier model capabilities.

• Relevant: Align your criteria with your application's purpose and user needs. Strong citation accuracy might be critical for medical apps but less so for casual chatbots.

Common success criteria

Here are some criteria that might be important for your use case. This list is non-exhaustive.

Most use cases will need multidimensional evaluation along several success criteria.

Build evaluations

Eval design principles

1. Be task-specific: Design evals that mirror your real-world task distribution. Don't forget to factor in edge cases!

   Example edge cases

2. Automate when possible: Structure questions to allow for automated grading (e.g., multiple-choice, string match, code-graded, LLM-graded).
3. Prioritize volume over quality: More questions with slightly lower signal automated grading is better than fewer questions with high-quality human hand-graded evals.

Example evals

Grade your evaluations

When deciding which method to use to grade evals, choose the fastest, most reliable, most scalable method:

1. Code-based grading: Fastest and most reliable, extremely scalable, but also lacks nuance for more complex judgements that require less rule-based rigidity.

   • Exact match: output == golden_answer
   • String match: key_phrase in output

2. Human grading: Most flexible and high quality, but slow and expensive. Avoid if possible.

3. LLM-based grading: Fast and flexible, scalable and suitable for complex judgement. Test to ensure reliability first then scale.

Tips for LLM-based grading

• Have detailed, clear rubrics: "The answer should always mention 'Acme Inc.' in the first sentence. If it does not, the answer is automatically graded as 'incorrect.'"
  A given use case, or even a specific success criteria for that use case, might require several rubrics for holistic evaluation.
• Empirical or specific: For example, instruct the LLM to output only 'correct' or 'incorrect', or to judge from a scale of 1-5. Purely qualitative evaluations are hard to assess quickly and at scale.
• Encourage reasoning: Ask the LLM to think first before deciding an evaluation score, and then discard the reasoning. This increases evaluation performance, particularly for tasks requiring complex judgement.

Next steps