RAG. Retrieval Augmented Generation.
No build today. Just learning. And honestly it was one of the most mind-expanding study sessions since I started this challenge.
What is RAG?
Every AI model I've worked with so far: Claude, GPT, any LLM, has a knowledge cutoff. It knows what it was trained on and nothing else. Ask it about your company's internal documents, your client's data, or anything that happened after training; it has no idea.
RAG fixes that.
Instead of relying purely on what the model was trained on, RAG retrieves relevant information from an external source; a database, a document store, a knowledge base — and passes it to the model as context before generating a response.
The model doesn't need to have been trained on your data. It just needs to be able to read it at the moment you ask.
Why This Changes Everything for Automation
Every automation I've built so far sends data directly to Claude in the prompt. That works for small, structured inputs.
But what about:
- A chatbot that answers questions about a company's entire document library?
- A support system that references hundreds of past tickets to resolve new ones?
- A report generator that pulls from months of historical data?
You can't paste all of that into a prompt. RAG is how you solve that.
What I Learned Today
The core RAG pipeline:
User query
↓
Search knowledge base for relevant chunks
↓
Pass retrieved chunks + query to LLM
↓
LLM generates response grounded in real data
The builds that are coming next are going to be on a completely different level.
No GitHub link today, just a study day documented publicly.
47 more to go.
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