Deployment Playbook: RAG source quality
How to prepare the workflow for CI/CD and production operations. how document quality, chunking, metadata, and citations affect answer quality
Practical writing on agentic systems, orchestration, automation design, and the operating patterns behind reliable AI delivery.
How to prepare the workflow for CI/CD and production operations. how document quality, chunking, metadata, and citations affect answer quality
The security and permission concerns that should be reviewed. how validation, policies, refusals, and human approval reduce risk
How engineering, product, and operations teams should collaborate. how to evaluate coupling, boundaries, state, and failure modes
How the workflow changes as traffic, users, or data volume grows. how local models support demos, privacy-sensitive tests, and offline workflows
A system-design view for planning production implementation. how declarative deployments reduce drift and improve reviewability
Why Model Context Protocol matters for connecting AI agents to databases, files, SaaS tools, and company workflows.
The mistakes teams should identify before launch. how to keep interactive AI pages fast, accessible, and stable
The security and permission concerns that should be reviewed. how document quality, chunking, metadata, and citations affect answer quality
How to measure quality, reliability, and operational readiness. how validation, policies, refusals, and human approval reduce risk
How to prepare the workflow for CI/CD and production operations. how to evaluate coupling, boundaries, state, and failure modes
A map of useful tools, libraries, and platform decisions. how local models support demos, privacy-sensitive tests, and offline workflows
Opinionated engineering notes for practical AI and software delivery. how deployment pipelines should handle prompts, evals, migrations, secrets, caches, queues, and scheduled AI jobs together