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Production AI articles

Cost, reliability, security, observability and deployment.

Getting an AI feature to work in a demo is a different problem from keeping it working for real users. Production AI covers what happens after launch: tracing requests so bad answers can be explained, keeping costs predictable, surviving provider outages and rate limits, and defending against prompt injection and misuse.

This category also covers how organisations adopt AI engineering at scale, including guardrails, changes to the software delivery lifecycle, and how developer workflows change when AI tools become part of daily work. The common thread is running AI systems responsibly and economically over time.

For a structured path through this topic, see Production AI Systems.

10 articles

Intermediate
AI Strategy for Engineering Leaders: Choose, Fund and Stop
How engineering leaders decide which AI projects to back: a portfolio view, stage gates, stop criteria and evidence-based reporting, with reusable scorecards.
Oct 11, 2026 9m
Intermediate
An AI Governance Framework for Engineering Teams
A lightweight AI governance framework for engineering teams: inventory, risk tiers, approvals, evaluation gates, incidents and a review cadence.
Oct 11, 2026 11m
Intermediate
API or Local Models: Where the Cost Break-Even Sits
When does running a model yourself beat paying for an API? A total-cost framework, an illustrative break-even calculation and the non-cost factors that decide.
Oct 11, 2026 9m
Intermediate
How to Measure the Impact of AI Coding Tools on Your Team
How to tell whether AI coding tools are working: measure delivery, quality and developer experience against a baseline, and avoid activity metrics that mislead.
Oct 11, 2026 10m
Intermediate
LLM Observability: What to Trace and Why
What to trace and log in a production LLM app: prompts, retrieved context, tool calls, tokens, cost and latency, with sampling, privacy and alerting guidance.
Oct 11, 2026 9m
IntermediateFeatured
The 90-Day Enterprise AI Adoption Playbook
A practical 90-day plan for enterprise AI adoption: foundation, controlled scale and production, plus a 5-level maturity model to show where you stand.
Sep 11, 2026 15m
IntermediateFeatured
Inside the AI-Augmented Developer Workflow
One ticket, start to finish, through an AI-augmented development workflow: where the agent helps, where it stalls and where a human has to step in.
Sep 11, 2026 13m
AdvancedFeatured
The AI-Native SDLC: What AI Does and What Humans Decide
Every stage of the software lifecycle rebuilt around AI: what AI now does, what changes and what stays human-controlled at each of twelve stages.
Sep 11, 2026 15m
AdvancedFeatured
Enterprise AI Guardrails: A Ten-Layer Defense in Depth
A ten-layer guardrail model for enterprise AI: prompt injection, least-privilege tools, approved models, audit trails, kill switches and MCP security.
Sep 11, 2026 17m
Intermediate
How to Reduce LLM Costs by 90% Without Hurting Quality
Semantic caching, model routing and context compression compound. How each lever works, how to apply it safely, the maths behind 90% and where teams go wrong.
Sep 4, 2026 11m

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