Enterprise RAG Evaluation Pipeline Project
Imaad Mahmood
🚀 Enterprise RAG Evaluation Pipeline (LLMOps)
Welcome to the Enterprise RAG Evaluation Pipeline! This project is designed to solve one of the biggest bottlenecks in modern AI: Trust.
Whether you are a recruiter looking at my architectural decisions, or a beginner trying to understand how to build AI systems that don't hallucinate, this guide will walk you through the entire process.
📌 The Business Problem (For Hiring Managers)
Retrieval-Augmented Generation (RAG) is the industry standard for querying private corporate documents. However, enterprises hesitate to deploy AI because of hallucinations and poor retrieval.
If an engineer changes the chunking strategy, or a new embedding model is swapped in, how do you know if the AI is still giving accurate answers?
You cannot manually test every prompt.
✅ The Solution
This project is an end-to-end, locally hosted RAG pipeline built with an Automated Evaluation Suite. It proves that AI responses can be programmatically tested for accuracy before being deployed to production.
🧠 Beginner's Tutorial: How This Actually Works
If you are new to AI engineering, here is exactly what is happening under the hood, broken down into three phases.
Phase 1: Data Ingestion (src/ingest.py)
src/ingest.py)AI models like ChatGPT or Llama don't magically know your company's private rules. We must provide the data.
Process:
Take a raw text file (example: corporate security policy).
Split it into smaller readable sections (chunks).
Convert chunks into numerical representations using an Embedding Model.
Store embeddings inside a Vector Database (ChromaDB).
This enables semantic search instead of keyword search.
Phase 2: Retrieval & Generation (src/query.py)
src/query.py)When a user asks:
"What is the password policy?"
The pipeline:
Searches the vector database for relevant chunks.
Retrieves only the most relevant policy sections.
Sends those sections as context to Llama 3.2.
The model generates an answer grounded strictly in retrieved data using LangChain Expression Language (LCEL).
Result: grounded, factual answers instead of hallucinations.
Phase 3: Automated Evaluation (tests/test_eval.py)
tests/test_eval.py)This is the LLMOps layer — and the core innovation of this project.
We created automated tests that behave like an AI quality inspector.
The evaluation suite:
Sends predefined questions
Validates answers using strict
assert statementsConfirms required facts exist
Detects hallucinated responses
Prevents silent performance regressions
Example validation:
If retrieval or generation quality drops, CI tests fail immediately.
🏗️ Architecture Overview
🛠️ Tech Stack
Component Technology LLM Engine Local Llama 3.2 (Ollama) Embeddings Nomic-Embed-Text Orchestration LangChain Core (LCEL) Vector Database ChromaDB Evaluation Pytest Deployment Style Fully Local (Privacy-First)
Why Local Models?
✅ 100% data privacy
✅ Zero API cost
✅ Offline capability
✅ Enterprise-ready architecture
📂 Project Structure
🏃♂️ How to Run This Project Locally
1️⃣ Install Ollama (Local LLM Runtime)
Download:
Pull required models:
2️⃣ Setup Python Environment
3️⃣ Build the Vector Database
Run ingestion:
This will:
Load documents
Chunk text
Generate embeddings
Store vectors in ChromaDB
4️⃣ Query the System
Ask questions about the corporate policy and receive grounded answers.
5️⃣ Run Automated Evaluation (CI/CD Step)
You will see the system automatically test AI correctness in real time.
🔬 What This Project Demonstrates
This repository showcases production-level AI engineering skills:
Retrieval-Augmented Generation (RAG)
LLMOps evaluation pipelines
Deterministic AI testing
Local model deployment
LangChain LCEL architecture
AI reliability engineering
🎯 Why This Matters for Enterprises
Companies don't reject AI because models are weak.
They reject AI because they cannot measure trust.
This project introduces:
measurable correctness
automated regression testing
deployment confidence
AI systems become testable software systems.
🤝 Let's Connect
I built this project to demonstrate production-ready Machine Learning and LLMOps practices.
If you are looking for an engineer who understands how to move AI out of notebooks and into reliable production systems — let's talk.
🔗 LinkedIn: https://www.linkedin.com/in/imaad-mahmood/
⭐ If You Found This Useful
Consider giving the repository a star — it helps others discover practical LLMOps examples!
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Posted Mar 17, 2026
Built a RAG pipeline with automated evaluation for enterprise AI deployment.
