What is RAG?
Retrieval-Augmented Generation is a technique that combines the power of large language models with external knowledge retrieval to provide accurate, grounded responses.
The Problem with Traditional LLMs
Hallucinations
LLMs confidently generate false information based on training patterns.
"Citing non-existent research papers or making up statistics."
Stale Knowledge
LLM training has a cutoff date, missing recent information.
"Unable to answer questions about events after training."
No Source Attribution
Can't trace where information came from.
"Users have no way to verify claims made by the model."
How RAG Solves These Problems
The Core Insight
Instead of relying solely on what an LLM learned during training, RAG retrieves relevant information from your own documents and provides it as context for the LLM to use when generating responses.
This grounds the LLM's responses in actual data, reducing hallucinations and enabling access to private or recent information.
# Pseudocode
def rag_query(question, documents):
# 1. Find relevant context
context = retrieve(question, documents)
# 2. Augment prompt with context
prompt = build_prompt(question, context)
# 3. Generate grounded answer
return llm(prompt)
The RAG Pipeline: Step by Step
Document Ingestion
Documents are loaded and split into smaller chunks for processing.
- Split documents into overlapping chunks
- Preserve context at boundaries
- Handle multiple file formats (PDF, TXT)
Embedding Generation
Each chunk is converted into a vector embedding.
- Use embedding models (e.g., text-embedding-3-small)
- Capture semantic meaning in vectors
- Enable similarity comparisons
Vector Storage
Embeddings are stored in a vector database for fast retrieval.
- Index vectors for similarity search
- Use FAISS for efficient storage
- Support approximate nearest neighbors
Query Processing
User questions are embedded using the same model.
- Convert question to vector
- Match vector space with documents
- Prepare for similarity search
Context Retrieval
Most similar document chunks are retrieved from the vector store.
- Find top-k most similar chunks
- Rank by cosine similarity
- Return with similarity scores
Augmented Generation
LLM generates an answer using retrieved context.
- Inject context into prompt
- Generate grounded response
- Cite sources in answer
Why Companies Use RAG in Production
Use Cases
- Customer support chatbots with company documentation
- Internal knowledge bases for employees
- Legal document search and analysis
- Medical literature review systems
- Code documentation assistants
Benefits
- Accurate answers grounded in real documents
- Access to private and proprietary data
- Up-to-date information beyond training cutoff
- Auditable responses with source citations
- Lower cost than fine-tuning models
Ready to learn more?
Explore the architecture in detail or try the live demo.