#Capstone#LLMOps#CI/CD#Eval Gates#Blue/Green#Auto-Rollback#Syllabus

Project 05: EvalShip — Eval-Gated CI/CD with Auto-Rollback

Wrap all prior projects in a production LLMOps shell where every code or prompt change must pass eval-gated CI stages before deployment, with blue/green and auto-rollback.

May 28, 2026 at 12:00 PM2 min readFollowFollow (Hindi)

Topics You Will Master

An eval-gated CI/CD pipeline for LLM and agent services
Prompt-regression and cost/latency guards per service
Blue/green deployment with gradual traffic shifting
Auto-rollback on error-rate or eval-score breach
A unified golden-test-case repository across projects
Best For

Engineers turning prototype LLM/agent systems into reliably deployable services.

Expected Outcome

A live CI/CD pipeline gating every change, deploying via blue/green, and rolling back automatically when alarms fire.

Project Overview

EvalShip is the LLMOps shell that wraps every prior project. No code or prompt change reaches production without passing eval-gated CI stages; rollouts run blue/green, and monitoring auto-rolls back on a breach.

Pipeline Stages (Hard Eval Gates)

  • Stage 1 — Linting and unit tests.
  • Stage 2 — Prompt regression against a golden baseline.
  • Stage 3 — Evaluation-harness score gate per service.
  • Stage 4 — Cost and performance regression guard.
  • Gate — PR blocked if any stage fails (score-delta comment posted).
  • Stage 5 — On merge: container build, push, rolling deploy.
  • Stage 6 — Staging smoke tests against golden cases.

Eval Gate Thresholds

  • Minimum faithfulness and answer relevancy scores.
  • Zero prompt-snapshot regressions.
  • Tokens-per-request, cost, and latency percentiles within tolerance of baseline.

Stack

  • A CI/CD platform for pipeline orchestration.
  • A prompt-regression framework with a golden snapshot store and rubric-based assertions.
  • An agent-native evaluation framework for multi-step traces.
  • A standardised benchmarking harness for base model evaluation.
  • A RAG evaluation framework for retrieval-quality metrics.
  • Calibrated LLM-as-Judge with domain-specific prompts.
  • Multi-stage container builds for all services with per-service registries.
  • A container orchestration service for stateless APIs and a model-serving platform for fine-tuned models.
  • Blue/green deployment with gradual traffic shifting.
  • Cloud monitoring with auto-rollback on error-rate breach.

Deployment & Observability

  • Tracing platform for per-call traces, token cost, and prompt versions.
  • A custom dashboard for LLM metrics.
  • Golden test cases committed as the regression baseline.

Deliverables

  • Golden test cases per project committed to the repository.
  • Per-project regression configs with rubric, cost, and latency assertions.
  • Full CI pipeline definition with all stages.
  • Multi-stage container files for all project services.
  • All services live on the cloud with appropriate serving infrastructure.
  • Monitoring dashboard with LLM metrics and an auto-rollback alarm configured.
  • Live demo showing a prompt change triggering the eval gate and a rollback.

Prerequisites

Modules 24–26 (prompting, context, evaluation harnesses & agent CI/CD) and the four prior capstone projects.

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