Generative artificial intelligence provides remarkable capabilities for synthesizing information, but general-purpose language models frequently generate convincing falsehoods when asked about private enterprise data. Sciematics Insights engineers production Retrieval-Augmented Generation (RAG) pipelines that enforce strict factual grounding.
Advanced Semantic Chunking
Naive fixed-size document chunking splits paragraphs mid-sentence, destroying semantic context. Our pipelines utilize structural document parsing that respects document hierarchy, section headings, and table boundaries. Chunks are enriched with contextual metadata prior to vector embedding generation.
Two-Stage Retrieval and Cross-Encoder Re-Ranking
To maximize precision without sacrificing latency, we employ a two-stage retrieval architecture: fast approximate nearest neighbor (ANN) vector search retrieves the top candidates, followed by a cross-encoder model that scores semantic relevance with high accuracy. Prompts are constructed using only strictly verified context passages.