RAG From Scratch: Building a Production Retrieval-Augmented Generation System

RAG is the most important architecture pattern in applied AI. Here's how to build one from scratch.

1. The Complete RAG Pipeline

[ Documents ] → [ Chunking ] → [ Embedding ] → [ Vector Store ]
                                                       |
                                              [ User Query ]
                                                       |
                                              [ Similarity Search ]
                                                       |
                                              [ Re-Ranking ]
                                                       |
                                              [ LLM Synthesis + Citations ]

2. Key Implementation Details

Chunking Strategy

function semanticChunk(document: string, maxTokens = 512): string[] {
  const paragraphs = document.split("\n\n");
  const chunks: string[] = [];
  let current = "";

  for (const para of paragraphs) {
    if (tokenCount(current + para) > maxTokens) {
      if (current) chunks.push(current.trim());
      current = para;
    } else {
      current += "\n\n" + para;
    }
  }
  if (current) chunks.push(current.trim());
  return chunks;
}

Retrieval with Re-Ranking

async function retrieve(query: string, topK = 5) {
  // Broad retrieval
  const candidates = await vectorStore.search(query, { topK: 20 });
  // Precision re-ranking
  const reranked = await crossEncoder.rerank(query, candidates);
  return reranked.slice(0, topK);
}

RAG grounds LLM responses in your actual data — eliminating hallucination while keeping knowledge current without retraining.