Building Your First RAG Pipeline
The Blueprint for Business AI
In this module, we take all the technical components we've learned and put them into a step-by-step workflow: from raw data to an answered question.
The "Librarian" Analogy
You stock the library shelves with books that have been carefully indexed by topic.
A student asks, "How do I file my GST?" The librarian finds the exact 3 pages on GST, hands them over, and says, "Use these to write your summary".
Phase 1: Preparation (Offline)
Before the AI can answer questions, you must prepare your data. This happens once (or whenever you add new files).
1. Ingestion
Collect PDFs, Excel, Notion docs.
2. Cleaning
Remove noise (headers/footers).
3. Chunking
Split text into 300-500 word blocks.
4. Storage
Save as Vectors in Weaviate/Pinecone.
Phase 2: The Action (The Query Loop)
When a user asks a question, this "Online" loop runs in real-time.
Vectorize Query
The AI turns the user's question into numbers.
Retrieval
It finds the "nearest neighbors" (most relevant text chunks).
Augmentation
The system stitches the question + retrieved chunks into one big prompt.
Generation
The LLM reads the context and answers.
Professional "No-Code" Tools (The Shortcut)
Dify / Langflow
Drag-and-drop tools to visually connect your files to an AI model.
n8n
Powerful automation that watches Google Drive and auto-indexes new files.
Pinecone Assistant
A "done-for-you" RAG service where you just drop files and get an instant search API.
Go to your AI Tutor and imagine this scenario:
A) Copy-paste the whole text into every prompt?
B) Build a RAG pipeline with chunking?"
Correct Answer: B!
RAG is faster, cheaper, and more accurate for large documents.