Markdown-Native Authoring Strategy for AI Knowledge Bases
Eliminating conversion loss upstream through Git-backed Markdown-native and semantic HTML knowledge authoring, AST-level chunking, and dual-use documentation.
Architectural Orientation
Part of the RAG Systems sub-track in Trust & Retrieval Engineering, Markdown-Native Authoring Strategy for AI Knowledge Bases defines the critical patterns and verification criteria needed for production reliability.
Key Engineering Principles
Enforce strict input sanitization, JSON schema compliance, and post-generation guardrails to maintain system predictability.
Bind all prompt modifications to automated regression evaluation suites with quantitative threshold pass/fail assertions.
Stream token usage, p95 latency, model confidence scores, and hallucination indicators directly to enterprise OpenTelemetry collectors.
Evaluate unstructured corpus conversion vs Markdown-native authoring ROI, linting pipelines, and AST chunking readiness.
Community Discussion & Feedback
Attributed peer feedback and official Netspective architecture notes.