What is RAG (Retrieval Augmented Generation)?

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ICSM Computer · Answer

RAG (Retrieval-Augmented Generation) is an AI/LLM technique where the model retrieves relevant external information
at query time and uses that information to generate a more accurate, up-to-date, and context-aware response.
Think of it as 
LLM + Search Engine +
Reasoning working together.
Why RAG Exists
LLMs (like GPT or other models) are trained on fixed datasets. They cannot know new facts or
organization-specific data unless they are updated or fine-tuned.
RAG solves this limitation by letting the model look things up before answering.
How RAG Works (Simple Flow)
User asks a question
Example: “Show me the refund policy of our product.”
Retrieve relevant documents
The system searches your database, PDFs, website, etc.Uses vector search, embeddings, or 
keyword search.
Augment the prompt
The question + extracted passages are combined.
Generate the answer
The LLM reads the retrieved data and produces an accurate, grounded answer.
Key Components of RAG
Embedding Model
Converts text → vectors (semantic meaning).
Vector Database
Stores embeddings and performs similarity search.
Examples: Pinecone, Weaviate, ChromaDB, Elasticsearch, Qdrant.
Retriever
Searches for relevant chunks of text using embeddings.
LLM Generator
The actual language model that forms the final response.
Example Use Cases
Customer Support Chatbots
Retrieve policy documents, FAQs, product details.
Enterprise Knowledge Search
Search internal documentation, emails, logs, wiki pages.
Legal and Compliance
LLM answers based on actual laws or policies (with citations).
Coding Assistants
Retrieve code snippets from repositories.
Medical / Finance Assistants
Retrieve trusted data sources instead of hallucinating.
Why RAG is Better Than Fine-Tuning for Knowledge
Feature
RAG
Fine-Tuning
Add new knowledge
Instant (update documents)
Requires retraining
Accuracy
High (retrieves source data)
Depends on training
Cost
Low
Expensive
Explainability
Provides citations
Harder
Real-time updates
Yes
No
What RAG Does Not Do
It does not rewrite the model or give it new reasoning abilities.It does not update the LLM’s internal parameters.It only feeds relevant documents into the model at runtime.
Simple Example Prompt (Inside RAG)
User:
“Does product X have a lifetime warranty?”Retrieved Document:
“Product X includes a 1-year limited warranty. No lifetime warranty is provided.”Augmented Prompt to LLM:
“Using only the following information:
‘Product X includes a 1-year limited warranty. No lifetime warranty is provided.’
Answer the question: Does product X have a lifetime warranty?”LLM Output:
“No. Product X has a 1-year limited warranty, not a lifetime warranty.”
Summary
RAG = Retrieval + LLM generation
It gives you:
real-time knowledgeless hallucinationcontext-specific answerseasier updates
It is currently the most practical method for enterprise AI, 
chatbots, search systems, and AI apps.

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What is RAG (Retrieval Augmented Generation)?

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1 Answers

Why RAG Exists

How RAG Works (Simple Flow)

Key Components of RAG

Example Use Cases

Why RAG is Better Than Fine-Tuning for Knowledge

What RAG Does Not Do

Simple Example Prompt (Inside RAG)

Summary

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Feature	RAG	Fine-Tuning
Add new knowledge	Instant (update documents)	Requires retraining
Accuracy	High (retrieves source data)	Depends on training
Cost	Low	Expensive
Explainability	Provides citations	Harder
Real-time updates	Yes	No