What is RAG?
•RAG stands for Retrieval-Augmented
Generation
• Combines information retrieval with LLM
generation
• Enhances answers using external knowledge
• Common in AI chatbots and assistants
3.
Why RAG isNeeded
• LLMs have limited or outdated knowledge
• Reduces hallucinations
• Provides domain-specific answers
• Uses real-time or private data
4.
RAG Architecture
• Userquery is received
• Relevant documents are retrieved
• Context is sent to LLM
• LLM generates final answer
5.
Key Components
• Documentstorage
• Embedding model
• Vector database (FAISS, etc.)
• Large Language Model
6.
Workflow Steps
• Convertdocuments into embeddings
• Store embeddings in vector DB
• Retrieve relevant context for query
• Generate answer using LLM
7.
Advantages of RAG
•Improves answer accuracy
• Supports private data usage
• Reduces hallucination
• Scalable knowledge updates
8.
Applications of RAG
•Customer support chatbots
• Medical assistants
• Enterprise knowledge search
• Education assistants
9.
Tools Used inRAG
• LangChain / LlamaIndex
• FAISS / Pinecone
• Hugging Face models
• Open-source LLMs
10.
Conclusion
• RAG combinesretrieval + generation
• Improves reliability of AI systems
• Widely used in modern AI apps
• Key technology for enterprise AI
