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🔍 Retrieval Augmented Generation

Ground LLMs with external knowledge for accurate, up-to-date responses

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Introduction to RAG

🎯 What is RAG?

Retrieval Augmented Generation (RAG) is a technique that enhances LLM responses by retrieving relevant information from external knowledge sources. Instead of relying solely on training data, RAG systems fetch current, domain-specific information and inject it into prompts, dramatically improving accuracy and reducing hallucinations.

💡
Core Insight

RAG bridges the gap between LLM capabilities and real-world knowledge. It enables models to answer questions about proprietary data, recent events, and specialized domains.

📚
Knowledge Access

Query private databases, documents, and real-time data sources

✅
Accuracy Boost

Ground responses in facts, reducing hallucinations significantly

🔄
Always Current

Access up-to-date information without retraining models

🔄 How RAG Works

1
Index Documents

Convert documents to embeddings and store in vector database

2
Retrieve Context

Search vector DB for documents similar to user query

3
Augment Prompt

Inject retrieved documents into LLM prompt as context

4
Generate Response

LLM produces answer grounded in retrieved information

✅ Advantages

  • •No model retraining needed
  • •Works with proprietary data
  • •Citable source attribution
  • •Cost-effective scaling

⚠️ Challenges

  • •Retrieval quality critical
  • •Context window limitations
  • •Embedding model selection
  • •Chunking strategy matters