RAG SYSTEMS: THE COMPLETE GUIDE
Retrieval Augmented Generation - Revolutionizing AI Applications
By UDDIT, AI Engineer & NIT Jaipur Graduate
WHAT IS RAG?
Retrieval Augmented Generation (RAG) is a cutting-edge AI technique that combines the power of large language models with external knowledge retrieval. This revolutionary approach enables AI systems to access and utilize vast repositories of information beyond their training data.
As an AI engineer specializing in RAG systems, I have built multiple production applications including patent search tools, document analyzers, and financial planning systems that serve hundreds of users daily.
✎ EDITOR'S NOTE
"RAG represents the future of enterprise AI - combining the reasoning of LLMs with the accuracy of retrieval systems."
MY RAG PROJECTS
Patent Drafting Tool
State-of-the-art RAG for prior art search & patent claims generation using Pinecone, OpenAI, LangChain.
UdditDoc-GPT
Offline RAG document analyzer with ChromaDB and Ollama for private deployments.
VECTOR DATABASES
Vector databases form the backbone of any RAG system, storing embeddings for lightning-fast similarity search across millions of documents.
| DATABASE | BEST FOR |
|---|---|
| Pinecone | Managed, Scaling |
| ChromaDB | Open-source, Offline |
| Weaviate | GraphQL, Hybrid |
| FAISS | Speed, Local |
| Qdrant | Performance |
EMBEDDINGS
Embeddings convert text into numerical vectors that capture semantic meaning. The choice of embedding model significantly impacts retrieval quality.
- ‣ OpenAI text-embedding-3-large - 3072 dimensions, state-of-the-art accuracy
- ‣ Cohere Embed v3 - Excellent for semantic search applications
- ‣ Sentence Transformers - Open-source and customizable
- ‣ Voyage AI - Domain-specific embeddings
CHUNKING STRATEGIES
Proper text chunking is critical for RAG performance. The way you split documents directly affects retrieval accuracy and response quality.
CHUNKING METHODS
- ■ Fixed-size: Simple, consistent chunks of 512-1024 tokens
- ■ Semantic: Based on content meaning and context
- ■ Recursive: LangChain's RecursiveCharacterTextSplitter
- ■ Document-aware: Preserves structure like headers
WHY HIRE ME?
- ✓ Production RAG systems serving 500+ users
- ✓ Expert in Pinecone, ChromaDB, Weaviate, FAISS
- ✓ Specialized in chunking & embedding optimization
- ✓ LangChain & LlamaIndex expertise
- ✓ NIT Jaipur AI/ML graduate
☎ CONTACT THE AUTHOR
udditalerts247@gmail.com
linkedin.com/in/lorduddit-
uddit.site
COMING NEXT ISSUE:
- › Building RAG from scratch
- › Vector DB selection guide
- › Advanced chunking tutorials
- › Hybrid search techniques