LLM Evaluation Pipeline with Golden Dataset and LLM-as-a-Judge
Build a reusable evaluation pipeline for LLM applications: a golden dataset, automated scoring with LLM-as-a-judge, and regression testing you can point at any prompt or model change to catch quality drops before users do.
This project was designed by data engineering professionals to simulate real-world scenarios used at companies like Netflix, Airbnb, and Spotify. Master Python, OpenAI API, Anthropic API and 2 more technologies through hands-on implementation. Rated intermediate level with comprehensive documentation and starter code.
🧪 Project: LLM Evaluation Pipeline with Golden Dataset and LLM-as-a-Judge
📌 Project Overview
Evaluation is the single most underrated, most-requested skill in AI engineering. Almost every senior AI engineer job description asks for experience designing eval pipelines, golden datasets, and LLM-as-a-judge workflows. In this project you'll build exactly that: a reusable harness that scores the output of an LLM task, so any prompt or model change becomes a measurement instead of a guess.
This is the project that ties the whole roadmap together. Once you have it, you can evaluate the RAG and agent projects, justify model choices, and prove your systems work.
🎯 Learning Objectives
- Define evaluation criteria and a written evaluation guideline for a task
- Build a golden dataset of representative inputs with known-good expectations
- Implement multiple scoring methods: exact match, embedding similarity, and LLM-as-a-judge
- Validate the LLM judge against human labels and mitigate its biases
- Wire the eval into a regression test that fails on quality drops
- Compare prompts and models on the same dataset and report the winner
🧰 Prerequisites
- Python 3.10+ and a virtual environment
- An LLM API key (OpenAI or Anthropic)
- A task to evaluate — reuse one you care about: a summarizer, a classifier, an extraction prompt, or the answer step of your RAG project
⌛ Estimated Time
Duration: 6–10 hours
Difficulty: Intermediate
🎯 Pick a Task to Evaluate
Choose a task with outputs you can judge:
- Structured extraction (e.g. pull fields from invoices/emails into JSON) — has checkable ground truth
- Summarization — open-ended, good for practicing LLM-as-a-judge
- Classification / routing — exact-match friendly
- RAG answers — reuse the RAG project's output and evaluate faithfulness
The harness should be task-agnostic: the same pipeline runs any task by swapping the dataset and the scoring config. That reusability is the point.
📦 Suggested Project Structure
llm-eval/
├── data/
│ └── golden.jsonl # inputs + expected outputs/rubrics
├── scoring/
│ ├── exact.py # exact match / functional correctness
│ ├── similarity.py # embedding-based similarity
│ └── judge.py # LLM-as-a-judge with rubric
├── pipeline/
│ ├── run_eval.py # run a system over the dataset, score, aggregate
│ └── compare.py # A vs B (prompt or model) comparison
├── tests/
│ └── test_regression.py # pytest gate on a quality threshold
├── reports/
├── README.md
└── .env.example
🔄 Step-by-Step Guide
1. 📝 Write the evaluation guideline
- Define, in writing, what "good" means for your task: the criteria (e.g. correctness, faithfulness, format, tone), what counts as a pass, and tricky edge cases. Without this, scores are noise.
2. 📚 Build the golden dataset
- Collect 30-50 representative inputs covering normal cases, edge cases, and known-hard cases.
- For each, record the expected output and/or a rubric. Store as
jsonlso it's diffable in git and grows over time.
3. ⚙️ Implement scoring methods
- Exact / functional: for checkable tasks (JSON field match, code that runs, label equality).
- Embedding similarity: for free-text where wording varies but meaning matters.
- LLM-as-a-judge: a strong model scores each output against your rubric and explains its reasoning. Support both absolute scoring and pairwise comparison.
4. ✅ Validate the judge
- On a sample, compare the LLM judge's scores to your own human labels. Measure agreement.
- Mitigate known biases: randomize order (position bias), control for length (verbosity bias), and avoid judging a model with itself where possible. Don't trust a judge you haven't checked.
5. 🔁 Build the pipeline and regression gate
run_eval.py: run your system over the golden set, score every example, and aggregate (mean scores, pass rate, per-criterion breakdown, failures list).test_regression.py: a pytest test that runs the eval and fails if the score drops below a threshold. Now a bad prompt change can't ship silently.
6. 🆚 Compare and report
- Use
compare.pyto run two prompts (or two models) over the same dataset and report which wins, by how much, and at what cost/latency. This is how you make model-selection and prompt decisions with evidence.
✅ Deliverables
- A written evaluation guideline for your task
- A golden dataset of 30-50 examples in
jsonl - Three scoring methods (exact, similarity, LLM-as-judge) behind a common interface
- A judge-validation note: agreement with human labels and the bias mitigations you applied
- A regression test (pytest) that gates on a quality threshold
- A comparison report (prompt A vs B, or model A vs B) with scores, cost, and latency
- A
README.mdexplaining the task, the criteria, the methods, and how to run it on a new task
🚀 Optional Extensions
- Add comparative (Elo-style) ranking across more than two systems
- Run the eval on a sample of live/production-style traffic to simulate monitoring
- Turn discovered failures into new golden examples automatically (the data flywheel)
- Wire the regression test into CI (GitHub Actions) so every pull request runs the evals
- Add per-criterion dashboards (pandas + a simple plot) to the report