Start Here: The AI-Native Engineering Canon

This site is a working knowledge base for developers building responsibly with LLMs.

Every post is part of a larger system — grounded in frameworks, artifacts, and workflows that help you ship real software with AI in the loop. No hype. No speculation. Just patterns that hold up under pressure.

This canon is designed to answer questions like:

• What actually changes when you build with AI?

• How do you evaluate, version, and govern prompts like code?

• What workflows scale — and what breaks silently?

You can explore this canon by framework, by problem, or by tool — depending on what you’re trying to solve.

Start Here

Take the AI-Native Scorecard (2 min quiz)
Get your current maturity level — and specific upgrade paths.
→ Take the Quiz

Core Frameworks

Each framework anchors a set of posts, reproducible workflows, and artifacts — and maps to one of six core engineering pillars:

• Prompt-to-Production

  A 6-stage development model — from vague spec to tested, shipped feature.

• AI-Native Developer Scorecard

  A tool-agnostic, behavior-based framework designed to help developers understand their current level of AI adoption.

• Prompt QA Lifecycle

  Add evaluation and regression testing to prompts like you would for code.

• PromptOps Lifecycle Governance

  Introduce versioning, logging, promotion, and rollback paths for production prompts.

• AI-Native Dev Stack

  A curated, stage-aligned toolchain for AI-assisted development.

• Workflow Swap Matrix

  Side-by-side transformations from legacy workflows to AI-native equivalents.

→ Browse all posts by tag

🧰 Solve a Specific Problem

Top Posts to Start With

• From Prompt to Production: A Developer Workflow for Building with AI
  The foundational workflow: 6 stages from vague spec to shipped code.

• How AI-Native Are You? A Framework for Devs Who Want to Build Smarter
  A 7-level maturity model that helps you diagnose your dev habits and gaps.

• The Modern Developer’s Guide to Upgrading Your Workflow with AI
  Side-by-side comparisons of how AI-native workflows differ from old habits.

GitHub Artifacts (Coming Soon)

Each framework links to installable assets — no-code prompts, CI configs, and scaffolds.

What This Canon Is Organized Around

Every post in this canon maps to one or more of six strategic content pillars. Each pillar reflects a specific lens on how AI changes software engineering. You’ll see these pillars referenced in post headers, tags, and framework maps. They keep the canon grounded — and make it reusable as a long-term system.

AI Fluency, Not Dependency

LLMs are powerful, but unpredictable. This pillar trains you to use them with awareness:

• Understand context limits, token behavior, and failure modes

• Avoid over-reliance on model output

• Build confidence in when to trust — and when to intervene

Posts here emphasize LLM behavior, guardrails, prompt patterns, and practical model fluency.

Architecting Beyond the Prompt

Prompts aren’t the system — they’re just one moving part. This pillar covers how to integrate AI into robust systems by focusing on:

• Modular design, retries, fallbacks

• Agent orchestration and routing

• Infrastructure-aware architecture

You'll learn how to avoid “prompt soup” and build things that scale and fail gracefully.

Feedback-First Engineering

AI outputs need to be evaluated — continuously. This pillar helps you:

• Add prompt evals to your CI/CD

• Log, score, and version prompt changes

• Catch regressions before they ship

Posts here reinforce PromptOps, changelogs, and eval-first design — treating prompts like code.

Critical Thinking in the Age of Confident Wrong Answers

LLMs sound convincing — even when they’re wrong. This pillar cultivates engineering skepticism:

• Spot hallucinations, bias, and logic errors

• Use test prompts and diagnostics

• Build failure-aware workflows
  The goal is not just correctness — it’s auditability and trustworthiness in AI-assisted output.

Coding for Context, Not Just Completion

Great AI-native workflows don’t just generate code — they generate the right code. This pillar focuses on:

• Prompting as user simulation

• Business-aligned definitions of done

• Translating vague intent into structured specs

You’ll see how to extract context, clarify goals, and improve alignment between product and implementation.

Own the Stack, Not Just the Code

AI-native devs don’t stop at writing prompts. They integrate AI into the full engineering stack:

• CI pipelines

• Observability tools

• Cost controls and performance monitoring

This pillar makes sure your AI systems are shippable, trackable, and governable in production.

Want a Shortcut?

Start with a tag:

• Prompt-to-Production

• AI-Native Developer Scorecard

• Prompt QA Lifecycle

• PromptOps Lifecycle Governance

• AI-Native Dev Stack

• Workflow Swap Matrix

Or view all posts →

This post updates as the canon evolves. New posts added weekly.