Making AI understand your proprietary data via RAG.
RAG (Retrieval-Augmented Generation) Engineer
Building custom AI search and chat systems using vector databases.
RAGVector DatabasesEmbeddings
Core Capabilities
Vector Database Integration (Pinecone, Supabase pgvector)
Document Chunking Strategies
Embedding Models
Semantic Search
Hybrid Search
Frequently Asked Questions
What is RAG and why do I need it?
RAG allows an LLM to accurately answer questions based on your private company documents (like PDFs, Notion docs, or internal wikis) without needing to retrain the model.
Which vector database do you use?
I typically use Supabase pgvector for unified relational and vector data, or Pinecone for dedicated, highly scalable vector search.