Introduction to RAG
Retrieval-Augmented Generation (RAG) combines the power of LLMs with external knowledge retrieval. Instead of relying solely on the model's training data, RAG fetches relevant information from a knowledge base in real-time.
Why RAG? LLMs have knowledge cutoffs and can hallucinate. RAG grounds responses in real, up-to-date information.
How RAG Works
1. User Query → Embed query into vector
2. Search → Find similar documents in vector DB
3. Retrieve → Get top-k most relevant chunks
4. Augment → Add retrieved context to prompt
5. Generate → LLM produces answer with context
2. Search → Find similar documents in vector DB
3. Retrieve → Get top-k most relevant chunks
4. Augment → Add retrieved context to prompt
5. Generate → LLM produces answer with context
Retrieval
Find relevant documents using semantic search (embeddings + vector DB).
Augmentation
Inject retrieved context into the LLM prompt as additional information.
Generation
LLM generates answer based on both its knowledge and retrieved context.
Simple RAG Pipeline
Conceptual example of a RAG system (simplified).
python
Output:
Click "Run Code" to see output
Benefits of RAG
- Up-to-date Information: Access latest data without retraining
- Reduced Hallucinations: Grounded in real documents
- Source Attribution: Can cite where information came from
- Domain Expertise: Add specialized knowledge easily
- Cost-effective: Cheaper than fine-tuning for knowledge updates