Structure RAG documents for vector search

RAG quality depends on chunk boundaries. When source documents are unstructured walls of text, embeddings mix unrelated facts and retrieval returns noisy context. aiStructEdit treats each knowledge article as a sectioned document with explicit scope per block.

Define article purpose, facts, exclusions, and citation format as separate sections. Cross-link related articles so the influence graph shows which blocks depend on which sources.

Export Markdown or XML chunks sized for your embedding pipeline. Run analysis for duplicates and token budget before indexing.

From corpus to vector index

Projects keep one instruction document per agent or product area. Duplicate detection finds overlapping FAQ entries before they pollute your vector store.

Pair structured RAG docs with agent system prompts in the same workspace — behavior rules in one project, knowledge corpus in another, both using the same section model.