Technical Reference | AI Engineering Playbooks

A practical relaunch plan to modernise Technical Reference into an AI-first engineering publication. Current site review Site is currently on a legacy Blogger layout ( Awesome Inc. theme), with old structure and low scannability. Most recent post is from December 2013. Archive is valuable but outdated for current engineering and AI workflows. Topic fit today should shift from ad hoc tips to systematic, production-grade AI engineering guidance. New positioning Technical Reference becomes: "A practical AI engineering reference for builders: agents, MCP, frameworks, security, ethics, and production operations." Target audience Engineers building AI-enabled products. Technical leads evaluating agentic architecture choices. Teams in regulated environments (including insurance). Makers shipping rapid prototypes and turning ideas into products. Pillars (content architecture) AI Agents and MCP in production. Framework and stack comparisons. AI security...

A practical 14-day blueprint for turning one validated AI use case into a secure, testable micro-product with measurable outcomes. Start with one painful workflow and a measurable business outcome. Keep scope tight: one persona, one trigger, one successful output. Ship with controls for quality, security, and operability from day one. Many AI projects fail because they start broad, not because the technology is weak. A micro-product approach keeps delivery disciplined and outcome-focused. This blueprint is designed for small teams that need to prove value quickly and safely. Prerequisites One clearly owned business problem. Access to subject matter experts. Basic delivery stack (repo, CI, monitoring). A named product and engineering owner. 14-day plan Days 1-2: Define outcome and scope Choose one workflow with repeated manual effort. Define baseline time, error rate, or cycle time. Write acceptance criteria for success. Days 3-4: Design the minimal archite...

A practical operating guide for teams adopting AI quickly without compromising quality, security, or trust. AI adoption succeeds when teams are explicit about boundaries, not just enthusiastic about tools. Do Define approved use cases and forbidden use cases. Keep a human reviewer for high-impact outputs. Use versioned prompts and templates for repeatable workflows. Capture and review model failures weekly. Validate outputs against source systems before action. Treat AI tooling access as privileged access. Don't Do not let AI-generated output bypass review in regulated workflows. Do not mix sensitive data into prompts without policy controls. Do not assume model confidence equals correctness. Do not ship agentic workflows without observability. Do not optimise for speed at the expense of rollback readiness. Team operating model Product sets problem and success metric. Engineering owns architecture and controls. Security signs off on tool boundaries. ...

A practical list of AI use cases for insurance operations, underwriting support, claims, and service workflows. Insurance teams do not need moonshot AI programmes to create value. They need targeted workflows with clear controls. 10 practical use cases Submission triage and document completeness checks. Broker email summarisation with action extraction. Underwriting note drafting from structured risk inputs. Claims intake classification and routing support. Policy wording comparison assistance. Renewal packet preparation and variance summaries. Internal knowledge retrieval for servicing teams. Meeting preparation briefs for account and placement teams. Escalation risk early-warning summaries. Portfolio-level trend summaries for leadership reviews. Delivery pattern that works Start with one workflow and one business metric. Add human-in-the-loop review for all externally visible outputs. Capture failure cases weekly and retrain prompt/process contracts. Exp...

This site is now focused on practical AI engineering: agents, MCP, frameworks, security, ethics, and product delivery. If you are building with AI and need practical, implementation-first guidance, this site is for you. Technical Reference is now focused on modern AI engineering with one rule: Useful in production beats impressive in demos. What you will find here AI agent architecture and MCP integration guides. Framework comparisons that include trade-offs, not marketing claims. Security and ethics checklists for real delivery contexts. Vibe coding workflows with reliability guardrails. AI + insurance applied patterns. Idea-to-product execution playbooks. How to use this site Start with implementation guides. Use comparison posts to choose a stack. Apply checklists before release. Revisit trend posts as the ecosystem changes. Publishing rhythm Tuesday: technical implementation guide. Friday: strategic comparison, trend, or case-based post. What to re...