Building a Simple RAG Application
My journey into Retrieval-Augmented Generation (RAG) and how I built a simple app to augment LLMs with custom data.
Why RAG?
Large Language Models (LLMs) are incredibly powerful, but they have a significant limitation: they only know what they were trained on. If you ask an LLM about a document you just wrote or breaking news from five minutes ago, it will hallucinate or tell you it doesn’t know.
That’s where Retrieval-Augmented Generation (RAG) comes in. It’s a technique that allows you to provide the LLM with relevant information from your own data sources before it generates an answer.
I wanted to move beyond just using ChatGPT and actually understand the mechanics of how we can ground these models in reality. So, I built Simple RAG.
What I Built
The application is straightforward:
- Ingestion: I take a document or a piece of text.
- Embedding: I convert that text into vector embeddings (numbers that represent the semantic meaning of the text).
- Storage: These vectors are stored in a vector database (or a simple in-memory store for this prototype).
- Retrieval: When a user asks a question, I convert the question into an embedding and find the most similar text chunks in my database.
- Generation: I feed the retrieved chunks + the user’s question into the LLM as context.
The “Aha!” Moment
The most interesting part of this process was seeing how much better the answers became when the model had context. It wasn’t just guessing anymore; it was synthesizing information I explicitly provided.
It also highlighted the challenges of “chunking”—how do you split up a document so that you retrieve exactly what’s needed without cutting off important context? That’s definitely an area for further optimization.
What’s Next?
This project was a practice run. Now that I understand the basics of the pipeline, I want to explore:
- Better Vector Databases: Moving from simple stores to robust solutions like pgvector or Qdrant.
- Hybrid Search: Combining keyword search with semantic search for better accuracy.
- Chat History: Giving the RAG system memory so it can handle follow-up questions.
If you’re interested, you can check out the live demo here: https://simple-rag.nyagah.me.
Learning by doing is the only way to really understand AI.