Build a Retrieval-Augmented Generation (RAG) System From Scratch
Build a RAG pipeline in Python that retrieves evidence, grounds answers, cites sources, and knows when the documents are not enough.
Build RAG as a Traceable Evidence System
A useful RAG system does more than place search results in a prompt. It must retrieve the right passages, preserve where they came from, enforce document permissions, and give the language model enough evidence to answer responsibly.
You'll build that complete path in Python, from document extraction and chunking through embeddings, hybrid retrieval, evidence assembly, citations, and evaluation. Keeping each stage visible makes it easier to find the real cause when an answer uses an outdated policy, misses an exact product identifier, cites the wrong passage, or responds without enough support.
The finished system is a private-document assistant that can answer from an authorized collection, show the evidence it retrieved, cite its sources, and decline when the documents don't support an answer.
What You'll Learn
Follow the evidence from the original document to the final answer while learning how to inspect and improve each layer.
Prepare Documents for Retrieval
Extract and clean text, preserve source identity and permissions, and create chunks that remain focused without losing the conditions needed to answer.
Build and Improve Retrieval
Create embeddings, calculate cosine similarity, inspect ranked results, and combine semantic search with exact terms, metadata filters, reranking, and diversity.
Generate Answers From Evidence
Assemble retrieved passages into a labeled evidence packet, connect claims to citations, and make clarification or abstention part of the response contract.
Evaluate the Correct Pipeline Layer
Measure retrieval, grounding, citations, and abstention separately so you can trace failures to chunking, ranking, filtering, generation, or validation.
Build and Test a Private-Document Assistant
The course includes the complete Python modules, sample policy documents, evaluation cases, Streamlit interface, and an interactive evidence lab. You can assemble the project file by file, test boundary cases, and inspect how changes to chunking, retrieval, filtering, and citations affect the result.
Who This Guide Is For
This intermediate project course is for Python developers and AI builders who want to understand and construct a RAG pipeline without relying on a framework that hides its behavior. You should be comfortable with basic Python, functions, lists, dictionaries, files, and virtual environments; the project also requires access to an embedding model and a language-model API.
Skills You’ll Develop
The broader competencies and practical skills this guide is designed to build.
AI Literacy & Governance
- AI Output Verification
- Responsible & Secure AI Use
- AI Capability Evaluation
- AI Fundamentals
Prompt & Context Engineering
- Context Engineering
Software Quality & Reliability
- Software Testing
- Debugging & Troubleshooting
Technical Career Development
- Technical Workflow Analysis
Python Development
- Functions & Modular Program Design
- Python File & Data Processing
Data Management & Analysis
- Numerical Computing with NumPy
Guide Sections
Begin with the problem RAG solves and the two pipelines that make it work.
Build a small, inspectable collection with reliable text and provenance before creating a single vector.
Turn documents into retrieval units that are focused enough to match and complete enough to answer.
Represent meaning numerically, calculate similarity, and inspect why the retriever chose each result.
Combine semantic similarity with exact terms, metadata, reranking, and diversity.
Turn retrieved chunks into an explicit evidence contract the model can follow and the application can verify.
Separate retrieval quality from answer quality so improvements target the component that failed.
Connect the pipeline to an interface while keeping indexing, retrieval, generation, and evaluation independently testable.
Manipulate one request through the whole pipeline, then create every project file and verify the system with boundary cases.
A RAG system is a controlled evidence pipeline around a language model, not simply a prompt containing search results.
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