Semantic Chunking & Context Engineering for Optimized RAG Pipelines
Poor chunking is the silent killer of RAG performance. We structure your data for maximum retrieval precision — semantic splitting, token-aware segmentation, and metadata enrichment that gives your vector database exactly what it needs to surface the right context, every time.
Output Format
{
"chunk_id": "doc_7f3a_chunk_042",
"text": "Retrieval-Augmented Generation (RAG) reduces hallucination by grounding LLM responses in verified external knowledge...",
"tokens": 847,
"metadata": {
"source_url": "https://example.com/rag-overview",
"section": "Architecture Overview",
"parent_doc_id": "doc_7f3a",
"position": 42,
"published": "2025-11-12T00:00:00Z"
},
"embedding_model": "text-embedding-3-large"
}The Problem
Most teams chunk by character count or sentence boundary, resulting in fragments that cut through arguments mid-thought. Vector search then retrieves these incoherent snippets, forcing the LLM to reason over incomplete context. The outcome: lower accuracy, more hallucinations, and user distrust of the system.
Our Solution
Our context engineers analyze your document corpus and design a chunking strategy that preserves semantic completeness. We tune chunk size, overlap windows, and metadata fields to your specific retrieval architecture — whether you're running LangChain, LlamaIndex, or a custom retriever.
Core Capabilities
Semantic Chunking & Token-Aware Splitting
We split documents at semantic boundaries — paragraphs, sections, and concept transitions — not arbitrary character counts. Each chunk is sized between 512–1,024 tokens (configurable), preserving the logical coherence required for accurate RAG retrieval and minimizing context window waste.
Metadata Enrichment for Vector Search
Every chunk is delivered with structured metadata: source URL, document title, section heading, publication date, author, and a pre-computed embedding key. This enables faceted filtering in your vector database (Pinecone, Weaviate, Qdrant, pgvector) and dramatically improves retrieval precision.
Hierarchical Document Structure Preservation
For long-form content (legal documents, research papers, technical manuals), we maintain parent-child chunk relationships. Your RAG system can retrieve a precise passage and then expand to the surrounding section for full context — without re-processing the entire document.
Improve your RAG retrieval accuracy
Share your document corpus and retrieval architecture. We'll benchmark your current chunking strategy and return a recommendation with a sample optimized output.
Request a Free PoC