What is RAG?

Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by combining them with external knowledge retrieval. Instead of relying solely on the model's training data, RAG allows AI to access and use up-to-date, domain-specific information.

Think of it as giving your AI assistant a reference library it can search through before answering questions.

Why RAG Matters

The Problem with Standard LLMs

Standard LLMs have several limitations:

Knowledge Cutoff: They only know information from their training data

Hallucinations: They may confidently generate incorrect information

No Custom Data: They can't access your proprietary information

Outdated Facts: Information changes but the model doesn't

How RAG Solves These Problems

RAG addresses each limitation:

Real-time Information: Retrieve current data before generating responses
Grounded Responses: Answers are based on actual documents
Custom Knowledge: Add your own documentation, databases, or APIs
Verifiable Sources: Provide citations for generated content

How RAG Works

RAG operates in three main stages:

1. Indexing (Preparation)

Convert your documents into vector embeddings and store them in a database.

2. Retrieval (Finding Relevant Information)

When a query comes in, convert it to an embedding and find similar documents.

3. Generation (Creating the Response)

Combine the retrieved context with the original query and generate a response.

Implementation Example

Here's a simplified RAG workflow using COZHUB:

// 1. Create embeddings for your documents

const embeddings = await client.embeddings.create({

model: 'text-embedding-3-small',

input: documents

});

// 2. Store embeddings in your vector database

// (Pinecone, Weaviate, Qdrant, etc.)

// 3. When user queries, retrieve relevant context

// and pass it to the LLM

const response = await client.chat.completions.create({

model: 'gpt-4o',

messages: [

{ role: 'system', content: 'Answer based on the provided context.' },

{ role: 'user', content: query + context }

]

});

Best Practices

1. Chunking Strategy

Chunk Size: 500-1000 tokens for most use cases
Overlap: 10-20% overlap prevents context loss
Semantic Chunking: Split on paragraphs or sections when possible

2. Embedding Model Selection

Model

Dimensions

Best For

text-embedding-3-small	1536	Cost-effective, most use cases
text-embedding-3-large	3072	High accuracy requirements

3. Retrieval Optimization

Hybrid Search: Combine vector and keyword search
Re-ranking: Use a cross-encoder to re-rank results
Query Expansion: Rephrase queries for better retrieval

4. Generation Tips

Always include source attribution
Set appropriate temperature (0.3-0.7 for factual tasks)
Use system prompts to enforce grounding

Common RAG Use Cases

Customer Support: Answer questions from documentation

Legal Research: Search through contracts and regulations

Knowledge Management: Query internal company knowledge

Research Assistants: Search academic papers and reports

Vector Database Options

Database