RAG WORKFLOWS

THE RAG PARADIGM

Large Language Models possess remarkable capabilities, but they remain bounded by their training data. For enterprises, the most valuable knowledge often exists in proprietary documents, internal wikis, and institutional memory that no foundation model has ever seen.

Retrieval Augmented Generation bridges this gap by dynamically injecting relevant context into LLM prompts. The system retrieves pertinent documents at inference time, grounding responses in organizational truth rather than statistical patterns from public internet data.

But naive RAG implementations fail spectacularly at scale. Chunking strategies that work for 100 documents collapse at 100,000. Embedding models optimized for general text miss domain-specific nuances. Production RAG demands engineering rigor.

SEMANTIC CHUNKING

The foundation of effective RAG lies in how documents are segmented. Fixed-size chunking—splitting text every N tokens—ignores semantic boundaries, often severing critical context mid-thought.

My approach implements semantic boundary detection using sentence transformers. The algorithm identifies natural breakpoints where topic shifts occur, preserving coherent units of meaning. Chunks maintain internal consistency while remaining appropriately sized for embedding models.

Overlap strategies are calibrated per document type. Technical documentation benefits from higher overlap to preserve cross-references. Conversational content requires less. The system adapts automatically based on document classification.

Metadata enrichment happens at chunk creation. Source documents, section headers, page numbers, and creation dates travel with each chunk, enabling precise source attribution in generated responses.

VECTOR ARCHITECTURE

Embedding model selection dramatically impacts retrieval quality. Through extensive benchmarking across client domains, OpenAI's text-embedding-3-large consistently outperforms alternatives for technical and legal content while maintaining cost efficiency.

Pinecone serves as the vector store of choice for production deployments. Its serverless architecture eliminates infrastructure management while providing millisecond query latency at scale. Namespaces enable multi-tenant isolation within single indexes.

Hybrid retrieval combines dense vector similarity with sparse BM25 matching. This dual approach captures both semantic relationships and exact keyword matches—critical for domains where specific terminology carries precise meaning.

Re-ranking pipelines apply cross-encoder models to candidate sets, dramatically improving precision for the final context window. The computational cost is justified by measurable accuracy gains.

PATENT DOMAIN RAG

Legal text presents unique challenges. Claim language is dense, cross-references abundant, and precision non-negotiable. The RAG system built for patent prosecution handles 50,000+ documents with 94% retrieval precision on domain-specific queries.

Custom chunking preserves claim structure—independent claims stay unified while dependent claims maintain references to their parents. The embedding model was fine-tuned on patent corpus to capture legal nuances that general models miss.

FINANCIAL DOCS RAG

Financial planning documents contain tables, calculations, and regulatory references. Standard chunking destroys tabular relationships. The custom pipeline preserves table structure as markdown, enabling accurate retrieval of numerical data.

Temporal awareness is built into the retrieval layer. Queries about "current" regulations automatically scope to the most recent document versions while maintaining access to historical context when explicitly requested.