Workflow:Explodinggradients Ragas LLM Benchmarking
| Knowledge Sources | |
|---|---|
| Domains | LLMs, Evaluation, Benchmarking |
| Last Updated | 2026-02-10 06:00 GMT |
Overview
End-to-end process for benchmarking and comparing multiple LLM models on a specific task using Ragas experiments and custom metrics.
Description
This workflow covers the systematic comparison of different LLM models on a domain-specific task. It involves preparing a standardized benchmark dataset, defining task-specific evaluation metrics, running separate experiments for each model under identical conditions, and comparing results across models. The Ragas experiment framework handles parallel execution, result persistence, and structured comparison. This enables data-driven model selection for production deployments.
Usage
Execute this workflow when you need to choose between multiple LLM providers or model versions for a specific use case. You should have a well-defined task (e.g., discount calculations, code generation, classification) with a labeled dataset and clear success criteria. This workflow is essential before committing to a production LLM provider, when evaluating cost-performance tradeoffs, or when a new model version becomes available.
Execution Steps
Step 1: Prepare the Benchmark Dataset
Create or load a standardized dataset that represents the target task. Each sample should include the input prompt, expected output, and any additional context needed. The dataset should cover diverse scenarios and edge cases to ensure comprehensive model comparison.
Key considerations:
- Use the Dataset class with a typed Pydantic model for schema enforcement
- Include sufficient samples for statistical significance
- Cover edge cases and diverse difficulty levels
- The same dataset is used across all model experiments for fair comparison
Step 2: Define the Benchmark Prompt and Metrics
Create the task prompt that will be sent to each LLM, and define metrics for scoring responses. The prompt should be model-agnostic (no model-specific formatting). Metrics can be discrete (correct/incorrect) for accuracy measurement or numeric for graded scoring.
Key considerations:
- Task prompts should use consistent formatting across models
- DiscreteMetric works well for pass/fail accuracy benchmarks
- NumericMetric works well for graded quality assessments
- Metrics can combine LLM-based evaluation with deterministic checks
Step 3: Run Per-Model Experiments
Execute a separate experiment for each LLM model being benchmarked. Each experiment uses the same dataset and metrics but targets a different model via the model parameter. The @experiment decorator handles async execution, result collection, and persistence.
Key considerations:
- Pass the model name as a parameter to the experiment function
- Use llm_factory() to create model-specific LLM instances
- Each experiment is saved with a unique name (e.g., "benchmark_gpt4o", "benchmark_claude")
- Experiments run asynchronously for efficiency
Step 4: Aggregate and Compare Results
Load results from all model experiments and combine them for comparison. Align results by sample ID, compute per-model accuracy or aggregate scores, and generate comparison tables. Identify which model performs best overall and on specific subsets.
Key considerations:
- Results can be exported to pandas DataFrames and merged on a common ID field
- Calculate per-model accuracy as the fraction of correct responses
- The CLI provides rich formatted tables with delta indicators for comparison
- Export combined results to CSV for further analysis or reporting
Step 5: Select Model and Document Decision
Based on the comparison results, select the best-performing model for the target use case. Consider not just accuracy but also latency, cost, and failure modes. Document the decision and benchmark results for future reference using experiment versioning.
Key considerations:
- Use version_experiment() to create git commits tracking the benchmark
- Consider cost-performance tradeoffs (cheaper models may be acceptable for some tasks)
- Analyze failure patterns to understand model-specific weaknesses
- Re-run benchmarks when new model versions are released