Capstone Project~8 hrs

Automated LLM Evaluation Framework

Build an automated testing pipeline to evaluate LLM responses for accuracy, bias, and toxicity before deploying models to production.

4 Parts/10 Tools/1K+ Test Cases

EVAL_RUN_v2.4_FINAL

FAITHFULNESS

0.94

Status: Passed

RELEVANCY

0.88

Status: Passed

HALLUCINATION

0.02

Status: Safe

LLM-as-a-Judge Protocol

GPT-4o as Judge → chain-of-thought verification → faithfulness scoring

fig 1 — evaluation run metrics dashboard

TEST CASES

1K+

Automated Suite

LATENCY

<2s

Per Evaluation

JUDGES

Ensemble Consensus

COVERAGE

95%

Regression Detection

System Architecture

Three-layer architecture: frontend dashboard, async evaluation backend, and analytics storage.

FRONTEND

Next.js Dashboard
Recharts Viz
Real-time Updates

BACKEND

FastAPI Server
Celery Workers
WebSocket Support

DATA

PostgreSQL (Tests)
ClickHouse (Results)
Redis (Queue)

GitHub Webhook→Async Evaluation→Alert & Report

What You'll Build

A production-ready evaluation system used by top AI companies to ship with confidence.

Multi-Metric Eval Engine

Exact match, semantic similarity, and LLM-as-judge with custom rubrics and scoring criteria

Test Suite Management

Version-controlled test cases with tagging, filtering, bulk operations, and CRUD API

CI/CD Integration

GitHub Actions automation: eval-on-PR, quality gates, merge blocking, and PR comment bot

Monitoring Dashboard

Live results, historical trends, regression detection, and exportable compliance reports

Curriculum

4 parts, each with a working checkpoint. Ship incrementally, test continuously.

Evaluation Framework

2 hours

Build multi-metric evaluation engine with LLM-as-judge, custom benchmarks, and automated scoring pipelines.

Run evaluations with multiple metrics

Test Management

2 hours

Create test case CRUD API, test suite management with versioning, tagging, and bulk operations.

Manage test suites programmatically

Multi-Judge Evaluation

2 hours

Build ensemble evaluation with multiple LLM judges, consensus strategies, and inter-judge agreement analysis.

Run multi-judge evaluations with agreement metrics

CI/CD Integration

2 hours

Automate evaluations with GitHub Actions, PR comment bot, quality gates, and merge blocking on regressions.

Automated eval-on-PR with quality gates

Technical Standards

Production patterns you'll implement across the evaluation pipeline.

PERFORMANCE

1K+test cases

Async Celery workers process evaluations in parallel with ClickHouse analytics

RELIABILITY

3+LLM judges

Multi-judge ensemble with consensus strategies and inter-judge agreement scoring

AUTOMATION

CI/CDpipeline

GitHub Actions: eval-on-PR, quality gates, merge blocking, and Slack alerting

Environment Setup

Launch the full evaluation stack locally with Docker Compose.

llm-eval-system

# Clone the project & launch eval stack
$ git clone https://github.com/aide-hub/llm-eval-system.git
$ cd llm-eval-system

# Start FastAPI + PostgreSQL + ClickHouse + Celery
$ docker-compose -f docker-compose.eval.yml up -d

# Run your first evaluation suite
$ python -m eval run --suite smoke-test --judge gpt-4o

Tech Stack

FastAPINext.jsPostgreSQLClickHouseCeleryOpenAI APIGitHub ActionsRechartsTailwindCSSWebhooks

Prerequisites

Python & FastAPI (async APIs, background tasks)
React / Next.js basics for the dashboard
LLM API experience (OpenAI or similar)
CI/CD concepts (GitHub Actions, webhooks)

Related Learning Path

Deepen your understanding of LLM evaluation frameworks, metrics design, and production testing workflows.

LLM Evaluation Learning Path

Ready to build your eval system?

Start with Part 1: Evaluation Framework