Who should use the LLM evaluation workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for llm evaluation with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A demonstrably improved model version with documented performance gains.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A demonstrably improved model version with documented performance gains.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Weave (by Weights & Biases) to a documented evaluation plan with a curated test suite and clear success criteria. Then, you pass the output to vLLM to a complete dataset of model responses paired with prompts and metadata, ready for scoring. Then, you pass the output to Deepchecks to a quantitative scorecard showing each model's performance across all evaluation dimensions. Then, you pass the output to Argilla to a qualitative error taxonomy and human-validated insights on model strengths and weaknesses. Then, you pass the output to Evidently AI to a polished evaluation report with quantitative results, qualitative insights, and clear recommendations. Finally, Ludwig is used to a demonstrably improved model version with documented performance gains.
Define Evaluation Criteria & Test Suite
A documented evaluation plan with a curated test suite and clear success criteria.
Run Model Inference & Collect Responses
A complete dataset of model responses paired with prompts and metadata, ready for scoring.
Automated Metric Scoring
A quantitative scorecard showing each model's performance across all evaluation dimensions.
Human-in-the-Loop Qualitative Review
A qualitative error taxonomy and human-validated insights on model strengths and weaknesses.
Analyze Results & Generate Report
A polished evaluation report with quantitative results, qualitative insights, and clear recommendations.
Iterate & Improve (Optional Feedback Loop)
A demonstrably improved model version with documented performance gains.
Start by identifying the specific capabilities you need to evaluate (e.g., factual accuracy, instruction following, safety, tone). Create a test suite of diverse prompts covering edge cases, typical use cases, and adversarial inputs. This step ensures you measure what matters for your use case.
Why Weave (by Weights & Biases): Weave (Weights & Biases) provides prompt versioning and automated regression testing, which directly supports defining and managing evaluation criteria and test suites.
Deploy the LLM(s) you want to evaluate (e.g., fine-tuned model, baseline, competitor) and run inference on the test suite. Ensure consistent generation parameters (temperature, max tokens) across all models for fair comparison. Store responses with metadata (model version, prompt, parameters).
Why vLLM: vLLM is designed for high-throughput LLM inference and batch processing, directly meeting the need for running model inference and collecting responses.
Apply quantitative metrics to each response automatically. Use standard NLP metrics (e.g., BLEU, ROUGE, BERTScore) for reference-based tasks, and LLM-as-a-judge for open-ended quality (e.g., GPT-4 scoring relevance). Compute aggregate scores per dimension and per model.
Why Deepchecks: Deepchecks specializes in evaluating LLM outputs and comparing model versions, directly supporting automated metric scoring.
Select a subset of responses (e.g., 10-20% of the test suite) for human review, focusing on edge cases and low-scoring outputs. Human annotators provide free-text feedback and categorical labels (e.g., 'hallucination', 'off-topic'). This catches issues automated metrics miss.
Why Argilla: Argilla is built for RLHF data collection and model evaluation, making it ideal for human-in-the-loop qualitative review.
Combine automated scores and human feedback into a comprehensive report. Identify statistically significant differences between models, highlight failure patterns (e.g., poor handling of long context), and rank models by overall performance. Include visualizations (bar charts, confusion matrices) for clarity.
Why Evidently AI: Evidently AI provides data drift detection and LLM response evaluation, which can generate reports on model performance.
Use the evaluation findings to refine your model or prompts. For example, fine-tune on misclassified examples, adjust system prompts, or add guardrails. Re-run the evaluation on the improved version to measure progress. This step closes the loop for continuous improvement.
Why Ludwig: Ludwig supports LLM fine-tuning, which is key for iterating and improving model performance based on evaluation feedback.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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