RAG Pipeline Knowledge Curator

Retrieval-Augmented Generation (RAG) is only as good as the knowledge it retrieves. Even the most powerful language model will produce poor, inconsistent, or hallucinated answers if the underlying document corpus is poorly prepared, inconsistently chunked, or inadequately indexed. This AI assistant specializes in the knowledge curation layer of RAG systems — the critical work of selecting, cleaning, structuring, and preparing documents so that retrieval is accurate, relevant, and grounded.

The assistant helps you audit and prepare your document corpus for RAG ingestion. It advises on document selection criteria — which sources belong in the knowledge base and which introduce noise or contradiction — and guides you through cleaning and preprocessing decisions: removing boilerplate, resolving duplicate or conflicting content, standardizing formatting, and ensuring factual consistency across documents.

A significant part of RAG curation is chunking strategy — deciding how to split documents into retrievable units. This assistant explains the tradeoffs between fixed-size, semantic, hierarchical, and document-structure-aware chunking approaches, and helps you select and configure the strategy that best matches your query patterns and content types. It also covers metadata enrichment: adding source, date, category, and confidence tags to chunks so that retrieval filters and ranking systems can operate with precision.

The assistant addresses common RAG failure modes — including context window overflow, chunk boundary information loss, semantic drift between query and retrieved chunk, and temporal staleness — and provides actionable remediation strategies for each. It also guides you through knowledge base refresh cycles, helping you build a sustainable curation workflow as your document corpus evolves.

This tool is ideal for AI engineers building or improving RAG-based products, teams deploying enterprise AI assistants on internal documentation, developers troubleshooting poor retrieval quality or high hallucination rates, and knowledge managers tasked with maintaining the accuracy and currency of an AI system's underlying information.