RAG Systems: Bringing Context to AI Conversations

Retrieval-Augmented Generation (RAG) is the bridge between general AI and specialized knowledge.

The Problem with Generic AI

GPT-4 is incredibly powerful, but it:

Doesn't know your business specifics

Can't access real-time data

May hallucinate facts

Lacks source attribution

How RAG Solves This

RAG adds a knowledge retrieval layer:

1. User asks a question

2. System searches your knowledge base

3. Relevant information is retrieved

4. AI generates response using this context

Architecture Deep Dive

Document Ingestion Pipeline

Source Documents → Chunking → Embedding → Vector Store

Query Processing

User Query → Embedding → Similarity Search → Context Assembly → LLM Response

Vector Search Explained

Why Vectors?

Semantic meaning > keyword matching

"AI development" and "machine learning engineering" are similar

Traditional search might miss this connection

Vector embeddings capture meaning

Similarity Metrics

Cosine similarity (most common)

Euclidean distance

Dot product

Chunk Size Strategy

**Too Small:** Loses context

**Too Large:** Dilutes relevance

**Optimal:** 400-800 tokens with 100-token overlap

Production Considerations

Performance

Index your vectors (IVFFlat, HNSW)

Cache frequent queries

Use connection pooling

Accuracy

Test with real user questions

Monitor relevance scores

Implement fallback responses

Cost Management

Embedding API calls

Vector storage

LLM inference

Case Study

E-commerce company reduced support tickets by 65%:

10,000 product docs ingested

Average query response < 1.5s

92% accuracy vs. human agents

Getting Started

1. Identify your knowledge sources

2. Choose vector database (pgvector recommended)

3. Select embedding model

4. Implement retrieval logic

5. Test and iterate

Need help implementing RAG? [Get in touch](/contact) with our AI team.