Trustable AI Interactions Engineering
Last Audited: 2026-08-24
NUP AI-Native Verified
ISO 42001 Cl. 8.2
In Plain Language
Trustable AI Interactions Engineering focuses on the mechanisms required to deliver verifiable, citation-backed AI systems for regulated enterprise applications. It covers Markdown/HTML trust layers, multi-layered RAG architectures, SQL-native middleware (surveilr), Model Context Protocol (MCP), and prompt caching.
Core ConceptsFour LayersContext PlaybooksAI InteractionsTech StackContent Engineeringspec-kitOpenSpecClaude CodeTechnical Communications
Category 03 · Phase 2 TrackMigration in Progress
Knowledge Transformation & Grounded RAG Architectures
Achieving 98%+ accuracy in enterprise RAG requires more than dumping PDFs into a vector database. It requires structured document transformation into semantic Markdown/HTML trust layers, hybrid lexical and vector search, SQL middleware, and continuous evaluation feedback loops.
Planned Topics in this Track
Trustworthy AI in B2B Systems: Compliance, Security & Verification
Expectations Engineering and User Acceptance Testing for Probabilistic Outputs
Markdown and HTML as Trust Layers in Document AI
Custom "Script-per-Document" Technology Strategy
Vector Database and Enterprise RAG Strategy
The Journey to 98% Accurate RAG: Multi-Layered Retrieval
surveilr as SQL-Native AI Context and Data Middleware
Backend Design with Model Context Protocol (MCP) and AnythingLLM
AI Gateway: Unified API Management, Rate Limiting & Prompt Caching
Text-to-SQL as a Foundation for Trustworthy AI
Community Discussion & Feedback
Attributed peer feedback and official Netspective architecture notes.
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