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:

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

2. 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

#AI #GenerativeAI #RAG #LLM #LangChain #Python #ArtificialIntelligence