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Creating an Advanced Evaluation LLM Benchmark

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Start a New Benchmark

  1. Go to Benchmarking → Create.
  2. Choose Advanced as the benchmark type.
Choose Advanced Benchmark
  1. Select LLM as the model type.
Select LLM Model Type

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General Information

  • Benchmark Name — Give the run a clear, unique name.
  • Select Deployments — Pick one deployment to benchmark.
    Only one LLM deployment can be chosen at once.
Deployment Selection

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Dataset Configuration

  • Presigned Dataset Link — Provide a presigned URL path to your dataset file.
    • Only JSON files are supported.
    • If the dataset has more than 1000 rows, only the first 1000 datapoints will be used.
    • You can use the provided [sample dataset format] as a reference.

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LLM Configuration

  • Max Tokens — Defines the maximum number of tokens the model can generate in a response.
    Example: 1024 means the response will be capped at 1024 tokens.
    A higher value allows longer outputs but also increases resource usage.
  • Temperature — Controls the randomness/creativity of the model’s output.
    Range: 0 to 1
    • Lower values (e.g., 0.2) → More deterministic and focused responses
    • Higher values (e.g., 0.8) → More diverse and creative responses
    • Example: 0.7 balances creativity and consistency
LLM Configuration

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Evaluation Configuration

We provide a collection of pre-built evaluators that you can use immediately for your AI evaluation needs. Choose up to 3 evaluators for assessing model outputs. Evaluators can be selected from the following categories:
  • Programmatic
    Uses custom JavaScript or Python code to programmatically evaluate quality.
    Useful for deterministic checks (e.g., regex validation, schema conformance, rule-based scoring).
  • Human
    Relies on human reviewers to assess outputs based on subjective or nuanced criteria like:
    • Readability
    • Tone
    • Clarity
    • Relevance
    • Factual correctness
  • Statistical
    Uses traditional ML metrics for text comparison. Helpful for benchmarking against reference outputs.
  • AI-based
    Uses LLMs as judges with carefully designed prompts.
    Provides automated, scalable evaluation with high alignment to human judgment.
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