From Vibe Check to Verification: Automating RAG Evaluation Pipelines with Ragas and GitHub Actions

We have all been there. You build a Retrieval-Augmented Generation (RAG) prototype, ask it a few specific questions about your company's internal documents, and watch in awe as it spits out the perfect answer. You think, "This is ready for production." This is what we call the "Vibe Check."

But what happens when the context changes? What happens when you swap the embedding model, or when the vector database grows from 100 documents to 100,000? In traditional software development, we would never deploy code simply because it "felt right." We use unit tests, integration tests, and CI/CD pipelines. Yet, in the rush to adopt Generative AI, many organizations are skipping rigorous testing, leading to hallucinations and performance regression in production.

At Nohatek, we believe that to move from a proof-of-concept to an enterprise-grade solution, you must treat your prompts and retrieval logic like code. In this guide, we will explore how to automate your RAG evaluation pipeline using the Ragas framework and GitHub Actions, ensuring your AI applications remain robust, accurate, and reliable.

The Problem with the 'Vibe Check'

The "Vibe Check" is essentially manual testing based on human intuition. While necessary during the initial exploratory phase, it is catastrophically unscalable for production systems. LLMs are non-deterministic by nature; asking the same question twice might yield slightly different phrasings. Furthermore, a RAG pipeline has two distinct points of failure:

• Retrieval Failure: The system failed to find the relevant documents in your database.
• Generation Failure: The system found the documents but the LLM failed to synthesize the answer correctly (hallucination).

When a user reports a bad answer, how do you know which part broke? Without metrics, you are flying blind. If a developer tweaks the chunking strategy to fix one query, they might inadvertently break ten others. This creates a cycle of "whack-a-mole" development that stalls innovation and destroys confidence in the AI solution.

"You cannot improve what you cannot measure. In the world of LLMs, relying on intuition is a recipe for technical debt."

Enter Ragas: Metrics That Matter

To solve the evaluation crisis, we turn to Ragas (Retrieval Augmented Generation Assessment). Ragas is an open-source framework that provides a suite of metrics specifically designed for RAG pipelines. Instead of relying on human labelers, Ragas uses an LLM (like GPT-4) to evaluate the output of your application LLM.

Here are the core metrics you should be tracking:

• Faithfulness: Does the answer rely only on the retrieved context? This measures hallucinations.
• Answer Relevancy: Did the AI actually answer the user's question?
• Context Precision: Did the retrieval system rank relevant documents higher than irrelevant ones?
• Context Recall: Did the retrieval system find all the necessary information required to answer the question?

Implementing Ragas allows you to generate a quantitative score for your pipeline. Here is a simplified example of how this looks in Python:

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy

results = evaluate(
    dataset=eval_dataset,
    metrics=[
        faithfulness,
        answer_relevancy,
    ],
)

By running this evaluation against a "Golden Dataset" (a curated list of questions and ground-truth answers), you establish a baseline performance score.

Automating the Loop with GitHub Actions

Having metrics is great, but running them manually on a developer's laptop is not enough. To achieve true LLMOps (LLM Operations), we need to integrate this into our CI/CD pipeline. Every time a developer opens a Pull Request—whether they are changing the prompt template, the temperature setting, or the retrieval logic—we want to ensure the quality hasn't dropped.

We can utilize GitHub Actions to trigger a Ragas evaluation automatically. Here is the architectural flow:

1. Trigger: A developer pushes code to a feature branch.
2. Build: GitHub Actions spins up a container and installs dependencies.
3. Execute: The workflow runs a Python script that queries your RAG pipeline using your test dataset.
4. Evaluate: Ragas calculates the scores (Faithfulness, Precision, etc.).
5. Gate: If the scores drop below a defined threshold (e.g., Faithfulness < 0.85), the build fails.

This automated gatekeeping prevents regression. It empowers your team to experiment boldly with new models or vector stores, knowing that the CI pipeline will catch any degradation in quality before it reaches the end user.

Here is a conceptual snippet for your .github/workflows/rag-eval.yml file:

name: RAG Evaluation
on: [pull_request]

jobs:
  evaluate-rag:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
      - name: Run Ragas Evaluation
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: |
          pip install -r requirements.txt
          python scripts/evaluate_pipeline.py

Moving from a "Vibe Check" to automated verification is the defining line between a hobbyist project and an enterprise asset. By combining the analytical power of Ragas with the automation of GitHub Actions, you create a safety net that allows your AI initiatives to scale securely.

At Nohatek, we specialize in building these robust LLMOps infrastructures. We don't just build chatbots; we build reliable, measurable, and scalable AI systems that drive real business value. If you are ready to professionalize your AI development pipeline, or if you need assistance migrating your cloud infrastructure to support these workloads, our team is here to help.

Ready to stop guessing and start verifying? Contact Nohatek today to discuss your AI strategy.