Applied AI

RAG: Retrieval Augmented Generation

RAG gives LLMs access to external knowledge at query time, no retraining required. Step through the full pipeline and run live simulations to see exactly how retrieval grounds every answer.

Pipeline explored

Demo query 1

Demo query 2

Section 1: The RAG Pipeline

Click each step to expand its explanation. Explore all 8 to unlock the demo.

ingest

retrieve

augment

generate

Steps explored0/8

Section 2: Live Demo Simulation

Choose a preset or type your own query to watch the full RAG pipeline execute.

Section 3: RAG vs Fine-Tuning

Both extend LLM knowledge, but with very different trade-offs.

Aspect

RAG

Fine-Tuning

Knowledge update

Real-time

Retrain required

Cost

Low

High

Hallucination

Reduced

Same

Private data

Yes

Setup time

Hours

Days

Section 4: When to Use RAG

RAG shines whenever your LLM needs knowledge it was never trained on.

Customer Support Bot

Answer product questions by retrieving from docs, FAQs, and release notes, always up to date.

Internal Knowledge Base

Let employees query HR policies, runbooks, and wikis without reading every document.

Research Assistant

Search across hundreds of papers and surface relevant passages for any research question.

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Key Insight

The retrieval step is often the bottleneck. A bad embedding model or chunking strategy means the right context is never retrieved, and even the best LLM can't help then.

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