From Documents to Intelligent Answers: Building a RAG Agent from Scratch & Lessons Learned

Artificial Intelligence applications are rapidly moving beyond simple question-answering systems. Modern enterprise AI assistants need to understand internal documents, retrieve accurate information, and provide reliable answers based on company knowledge.

I started building agentic AI systems from scratch — not to theorize about them, but to actually write the code, break it, and understand it from the inside out. This post walks through the second project in that journey: a Retrieval-Augmented Generation (RAG) system, what I built, what tripped me up, and where I’m taking it next.

What is RAG?

Retrieval-Augmented Generation combines two capabilities:

Retrieval: The system searches a knowledge base and finds relevant information related to the user’s question.

Generation: The retrieved information is provided as context to an LLM, which generates a response based on that knowledge.

Instead of asking an LLM to remember everything, RAG allows the model to access external knowledge dynamically.

Technologies Used
Python, LangChain, Ollama (Local LLM), Embeddings, Vector Database, FastAPI.

What I Built

The core idea is to build a system that answers questions using only the content of a document, rather than whatever the underlying model already “knows.”

Documents → Text Splitter → Embeddings → Vector Store → Retriever → LLM → Answer

Document loading — a company policy document, loaded with LangChain’s TextLoader

Text Splitting — split into 200-character chunks with 50-character overlap, using RecursiveCharacterTextSplitter

Create Embeddings — generated with sentence-transformers/all-MiniLM-L6-v2 via HuggingFaceEmbeddings

Vector store — persisted in Chroma

Retrieval — top-2 most relevant chunks pulled per question

Generation — a strict prompt template that instructs the model to answer only from the retrieved context, run through Ollama’s tinyllama.

Lessons Learned

Building a RAG system taught me that successful AI applications are not only about selecting a powerful LLM. The quality of the final answer depends heavily on:

Document quality

Chunking strategy

Retrieval accuracy

Prompt design

Evaluation methods

Conclusion

RAG provides a practical foundation for building enterprise AI assistants that can use private knowledge while reducing hallucination risks. This project became the foundation for my next experiments with multi-agent workflows, MCP servers, and autonomous AI systems.

Source Code: https://github.com/srirdeevi/agentic-ai-portfolio