Who should use the LLM Evaluation and Monitoring Workflow workflow?
Teams or solo builders working on ai development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · AI Development
Evaluate, test, and monitor LLM applications in production using Deepchecks platform for auto-scoring, version comparison, and anomaly detection.
Deliverable outcome
A continuous improvement cycle where monitoring data directly drives model and prompt enhancements, validated by quantitative scores.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A continuous improvement cycle where monitoring data directly drives model and prompt enhancements, validated by quantitative scores.
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 Deepchecks to a documented evaluation framework with measurable criteria and alert thresholds ready for testing. Then, you pass the output to Ragas to a curated evaluation dataset with golden answers, ready for automated scoring. Then, you pass the output to Deepchecks to a scored evaluation report with per-sample metrics and aggregate statistics, identifying weak areas. Then, you pass the output to Deepchecks to a clear comparison report showing which metrics improved or regressed, with actionable insights on problematic prompts. Then, you pass the output to Deepchecks to a live monitoring dashboard with real-time anomaly detection and alerting, ensuring rapid response to quality degradation. Finally, Deepchecks is used to a continuous improvement cycle where monitoring data directly drives model and prompt enhancements, validated by quantitative scores.
Define Evaluation Criteria and Metrics
A documented evaluation framework with measurable criteria and alert thresholds ready for testing.
Generate and Curate Evaluation Dataset
A curated evaluation dataset with golden answers, ready for automated scoring.
Auto-Score LLM Outputs with Deepchecks
A scored evaluation report with per-sample metrics and aggregate statistics, identifying weak areas.
Perform Version Comparison and Regression Testing
A clear comparison report showing which metrics improved or regressed, with actionable insights on problematic prompts.
Deploy Monitoring and Anomaly Detection in Production
A live monitoring dashboard with real-time anomaly detection and alerting, ensuring rapid response to quality degradation.
Iterate and Improve Based on Monitoring Insights
A continuous improvement cycle where monitoring data directly drives model and prompt enhancements, validated by quantitative scores.
Identify the specific dimensions of LLM output quality relevant to your use case (e.g., accuracy, relevance, safety, tone). Select automated metrics (e.g., BLEU, ROUGE, BERTScore) and custom rubric criteria. Document thresholds for pass/fail and anomaly detection.
Why Deepchecks: Deepchecks provides a dedicated LLM Evaluation module with metric libraries and threshold configuration, directly matching the step's needs.
Create a representative dataset of input prompts and expected outputs (golden answers) that covers edge cases, common queries, and adversarial inputs. Use synthetic generation, human annotation, or production logs. Split into validation and holdout sets.
Why Ragas: Ragas includes synthetic test data generation, which directly supports generating and curating an evaluation dataset.
Run your LLM against the evaluation dataset and feed outputs into Deepchecks' auto-scoring pipeline. Configure scoring to compute selected metrics (e.g., semantic similarity, toxicity) for each output. Review aggregate scores and per-sample breakdowns.
Why Deepchecks: Deepchecks provides an auto-scoring pipeline for LLM outputs, directly matching the step's requirement for auto-scoring with metric config.
Compare scores from the current LLM version against a baseline (e.g., previous model version or prompt template). Use Deepchecks' comparison dashboard to detect regressions or improvements. Identify specific prompts where performance changed significantly.
Why Deepchecks: Deepchecks includes a version comparison dashboard and supports comparing model versions, directly addressing regression testing needs.
Integrate Deepchecks monitoring agent into your production LLM pipeline to continuously score a sample of live outputs. Configure anomaly detection rules (e.g., sudden drop in semantic similarity, spike in toxicity). Set up alerts (email, Slack) for real-time notification.
Why Deepchecks: Deepchecks provides monitoring for AI systems in production and can integrate with alerting systems like Slack and email.
Regularly review monitoring dashboards and anomaly logs to identify recurring issues. Use insights to update the evaluation dataset, refine prompts, fine-tune the model, or adjust thresholds. Re-run auto-scoring and version comparison to validate improvements.
Why Deepchecks: Deepchecks provides a monitoring dashboard and evaluation capabilities that support iteration and improvement based on insights.
§ Before you start
Teams or solo builders working on ai 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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