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From Use Case to Evaluation Pipeline in 10 Minutes

· 2 min read · Gaurav Kumar Sinha
evaluation mlops quality automation

Why Evaluation Matters

Every team deploying LLMs needs evaluation pipelines, but current approaches are inadequate. Manual testing doesn’t scale. Libraries like RAGAS require custom infrastructure. And most teams end up with no evaluation at all — quality degrades silently.

The Infrastructure Gap

Tools like RAGAS, DeepEval, and Promptfoo exist as libraries. They compute metrics. But nobody has built the infrastructure framework that auto-generates and deploys the entire evaluation pipeline as serverless infrastructure.

Describe, Don’t Build

What if you could describe your use case and get a complete evaluation pipeline?

use_case:
  type: rag
  description: "Customer support chatbot using RAG"

evaluation:
  metrics: auto  # framework selects based on use case type
  thresholds:
    faithfulness: 0.85
    answer_relevancy: 0.80

schedule:
  frequency: daily

From this config, EvalForge generates:

  • Metric selection optimized for your use case type
  • Synthetic test data (including adversarial cases)
  • Step Functions pipeline for scheduled execution
  • Drift detection with statistical significance testing
  • Alerts when quality degrades

The Pattern

Every LLM evaluation pipeline follows the same pattern: define use case, select metrics, generate test data, run evaluations, detect drift, alert. EvalForge automates this entire pattern.

What’s Next

EvalForge is part of the SubstrAI ecosystem, designed to integrate with LambdaLLM and PromptOps for end-to-end GenAI quality assurance on serverless infrastructure.