RAGConnect AI to Your Data
The simple version: RAG = AI + your data. It retrieves relevant info from your documents and uses it to generate accurate answers. Think of it as giving AI a cheat sheet for your specific questions.
What Is RAG?
What is RAG?
RAG (Retrieval-Augmented Generation) is a technique that makes AI smarter by giving it access to external information. Instead of relying only on what it learned during training, the AI can look up relevant documents, databases, or websites to answer your questions more accurately.
Why do we need RAG?
AI models like GPT have a knowledge cutoff — they don't know about recent events or your specific documents. RAG solves this by retrieving relevant information in real-time, so the AI can give accurate, up-to-date answers based on your actual data.
Is this like giving AI a search engine?
Kind of! But smarter. A search engine shows you documents. RAG retrieves the most relevant parts of documents and feeds them to the AI, which then synthesizes a comprehensive answer. It's search + understanding combined.
How RAG Works
A step-by-step look at what happens when you ask a RAG system a question
You Ask a Question
"What's our company's refund policy for software subscriptions?"
Search Your Knowledge Base
The system searches your documents, finding relevant policy pages, FAQ entries, and related content.
Retrieve Relevant Chunks
It extracts the most relevant paragraphs — not whole documents, just the useful parts.
Combine with Your Question
Your question + the retrieved information are sent to the AI together.
AI Generates Answer
The AI uses the provided context to generate an accurate, grounded answer.
Why Use RAG?
The key benefits of connecting AI to your data
Accurate Answers
AI responds based on your actual data, not just what it learned during training.
Up-to-Date Information
Access real-time data, recent documents, and current information.
Source Attribution
Know exactly where information came from — cite your sources.
Reduced Hallucination
AI is less likely to make things up when it has real data to reference.
Domain-Specific
Works with your company docs, research papers, or any specialized content.
Cost-Effective
No need to retrain expensive models — just connect them to your data.
Real-World Use Cases
Where RAG is making AI more useful
Customer Support
Answer customer questions based on your product docs, FAQs, and support history.
Internal Knowledge
Help employees find information in company wikis, handbooks, and documentation.
Legal Research
Search case law, contracts, and legal documents to answer specific questions.
Academic Research
Query research papers, textbooks, and academic sources for insights.
Sales Enablement
Access product specs, competitive info, and sales materials instantly.
Personal Knowledge
Query your notes, bookmarks, and saved articles for personal use.
Inside a RAG System
The main components that make RAG work (simplified)
Document Loader
Takes your documents (PDFs, Word docs, web pages, etc.) and prepares them for processing.
Analogy: Like scanning papers into a computer — making them readable by the system.
Text Splitter
Breaks documents into smaller chunks (paragraphs, sections) that are easier to search and process.
Analogy: Like cutting a book into index cards — each card has one idea.
Embedding Model
Converts text chunks into numbers (vectors) that capture meaning. Similar texts have similar numbers.
Analogy: Like assigning GPS coordinates to ideas — related ideas are close together.
Vector Database
Stores the embeddings and enables fast "similarity search" to find relevant chunks.
Analogy: Like a smart filing cabinet that finds documents by meaning, not just keywords.
Retriever
When you ask a question, it finds the most relevant chunks from the database.
Analogy: Like a research assistant who quickly finds the right books for your question.
Language Model
The AI (like GPT) that reads the retrieved chunks and your question to generate an answer.
Analogy: Like an expert who reads the materials and writes you a clear summary.
RAG vs Other Approaches
When to use RAG versus other techniques
RAG
PROS
- Uses real-time data
- No model training needed
- Easy to update content
- Source attribution
CONS
- Requires infrastructure
- Quality depends on retrieval
Best for: When you need current, factual answers from specific documents
Fine-tuning
PROS
- Model learns your style
- Faster inference
- No retrieval infrastructure
CONS
- Expensive to train
- Knowledge gets outdated
Best for: When you need the model to learn a specific tone or task pattern
Prompt Engineering
PROS
- No infrastructure needed
- Quick to implement
- Flexible
CONS
- Limited by context window
- No persistent knowledge
Best for: Simple tasks with small amounts of reference data
Key Terms
Embedding
A list of numbers representing the meaning of text. Similar meanings have similar embeddings.
Vector Database
A database optimized for storing and searching embeddings (vectors).
Semantic Search
Finding content by meaning, not just exact keywords. "Car" matches "automobile."
Chunk
A piece of a document (paragraph, section) used for retrieval. Not too big, not too small.
Context Window
The maximum amount of text an LLM can process at once. RAG helps work within this limit.
Retriever
The component that finds and returns relevant chunks for a given question.