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Chunking, Embeddings, and Vector Search

Beginner explanation​

RAG works only if documents are split into useful pieces. Chunking creates those pieces, embeddings make them searchable by meaning, and vector search finds the closest matches.

Production explanation​

Real systems treat chunking as a content engineering problem. The chunk boundary affects retrieval quality, citation clarity, token cost, and how often the model hallucinates from weak context.

Real-world enterprise example​

A policy assistant indexes HR handbooks, travel policy PDFs, and onboarding guides. Chunks must preserve section titles and document version so answers can cite the right rule.

Mermaid diagram​

TypeScript example​

export interface ChunkMetadata {
  sourceId: string;
  title: string;
  section: string;
  sourceType: 'doc' | 'ticket' | 'code' | 'transcript';
  updatedAt?: string;
}

export function chunkDocument(text: string, chunkSize = 800): string[] {
  const parts: string[] = [];
  for (let i = 0; i < text.length; i += chunkSize) {
    parts.push(text.slice(i, i + chunkSize));
  }
  return parts;
}

Python example​

def sliding_window_chunks(text: str, size: int = 800, overlap: int = 120) -> list[str]:
    chunks = []
    start = 0
    while start < len(text):
        end = start + size
        chunks.append(text[start:end])
        start += size - overlap
    return chunks

Common mistakes​

  • chunks that are too large to stay semantically tight
  • chunks that lose section headers and source identity
  • storing embeddings without enough metadata for filtering and debugging
  • indexing OCR noise and layout artifacts as if they were real content

Mini exercise​

Test three chunking strategies on one document and compare which one produces the clearest citations for five sample questions.

Project assignment​

Define your ingestion chunk format and metadata schema for Project: Enterprise RAG Copilot.

Interview questions​

  • What tradeoffs change when chunk size gets larger or smaller?
  • Why is overlap sometimes useful?
  • What metadata is essential for production debugging?

Monetization angle​

Document ingestion quality is one of the least visible and most valuable parts of enterprise RAG work. Teams often underestimate how much this determines outcomes.