Getting Started with RAG

Retrieval-Augmented Generation (RAG) is the architecture behind most modern AI assistants that need to answer questions about specific documents, knowledge bases, or proprietary data.

What RAG Solves

Large language models have a knowledge cutoff and no access to your private data. RAG bridges this gap by retrieving relevant document chunks at query time and injecting them into the model's context window.

The core pipeline looks like this:

1. Ingestion — split documents into chunks, embed each chunk, store in a vector database
2. Retrieval — embed the user's query, find the closest chunks by cosine similarity
3. Generation — pass retrieved chunks + query to the LLM, get a grounded answer

Production Considerations

In a production RAG system, the naive pipeline breaks down quickly. Key challenges:

• Chunk size matters — too small loses context, too large dilutes relevance
• Embedding model choice — domain-specific models often outperform general ones
• Reranking — a two-stage retrieval (dense + rerank) significantly improves precision
• Hallucination detection — always verify cited sources exist in retrieved chunks

Our Stack

At JadayLix Studio, we build RAG systems with LangChain for orchestration, pgvector or Pinecone for vector storage, and OpenAI or open-source models depending on data sensitivity requirements.

The architecture is always modular — the vector store, embedding model, and LLM are all swappable behind clean interfaces. This is non-negotiable for production systems.