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AI Engineering Academy · Lesson

Building a Continuous Evaluation Pipeline

Integrate LLM-as-judge evaluation into your CI/CD pipeline so every prompt or model change is automatically evaluated against a regression test suite before deployment.

Why Evaluation Must Be Continuous

One-time evaluation at deployment is not enough. LLM quality degrades silently: model providers update their models, system prompt changes slip in, retrieval quality shifts as the document corpus grows, and user query distribution changes over time. A continuous evaluation pipeline runs the same suite of evaluations on every change and on a schedule so quality regressions are caught within hours, not weeks.

Core Components of the Pipeline

A continuous evaluation pipeline has five components: a test dataset (curated questions with expected outputs), a system runner (calls your LLM pipeline for each test question), a judge (scores each response), a results store (database or time-series store for historical metrics), and a reporting layer (dashboards and alerts). Each component is independently upgradeable.

# Pipeline architecture:
#
# test_dataset.json
#       |
#       v
# system_runner.py  --> calls your LLM pipeline
#       |
#       v
# judge.py          --> scores each (question, answer) pair
#       |
#       v
# results_db        --> stores timestamped metric history
#       |
#       v
# dashboard + alert --> Grafana / Slack notification

All lessons in this course

  1. The LLM-as-Judge Pattern
  2. Pointwise and Pairwise Evaluation
  3. Calibrating Judge Models Against Humans
  4. Building a Continuous Evaluation Pipeline
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