A practical guide to secure internal AI search over company documents with permissions, citations, data boundaries, logging, and evaluation.
LLM Evaluation Framework: How to Test AI Apps Before Launch
A practical LLM evaluation framework for testing AI applications before launch with golden data, automated checks, review, and release gates.
Prompt Engineering for Production Apps: Patterns That Matter
A practical prompt engineering guide for production AI applications, focused on structure, context, evaluation, versioning, and reliability.
Vector Database vs Knowledge Graph: Which Is Better for AI Search?
A practical comparison of vector databases and knowledge graphs for AI search, including RAG retrieval, relationships, permissions, and hybrid designs.
How to Build an Enterprise AI Roadmap That Actually Ships
A practical enterprise AI roadmap for leaders and engineering teams who want pilots that move into production instead of staying in slide decks.
AI App Architecture: Frontend, API, Model Layer, and Data
A practical guide to AI app architecture, covering frontend UX, API design, model orchestration, data controls, retrieval, evals, and monitoring.
MCP Explained: How AI Agents Connect to Tools and Data
A plain-English explanation of Model Context Protocol, how it helps AI agents connect to tools and data, and what teams should secure.
How to Monitor LLM Apps: Logs, Traces, Evals, and Alerts
Learn how to monitor LLM applications with logs, traces, evaluations, feedback loops, cost metrics, latency tracking, and actionable alerts.
Kubernetes for AI Workloads: What Actually Matters
A practical guide to Kubernetes for AI workloads, focused on what teams actually need for GPUs, model services, queues, scaling, and operations.
How to Add ChatGPT to an Internal Business App
A practical architecture guide for adding ChatGPT-style AI to internal business applications with the right controls and rollout plan.