Build with AI: Mindset, Habits & Tools for the Next 20 Years

Inject Raw Schemas: Stop describing data vaguely. Learn to inject raw CREATE TABLE statements to force 100% accurate SQL generation.

The "Context Injection" Protocol: Master the shift from "Prompting" to "Engineering" (feeding), treating AI as a deterministic engine.

Black Box to Co-Creator: Apply the 5-stage framework to diagnose why AI fails and fix the input data, not the tone of your prompt.

"Edit, Don't Chat" Rule: Stop "Context Contamination" by editing previous prompts instead of adding corrective layers that confuse the model

Manage Context Saturation: Learn to spot when the "Intern" (AI) gets tired and reset the window before performance degrades into laziness.

The 3-Turn Principle: Implement a strict workflow where you rewrite the prompt if the AI hasn't solved the problem within three turns.

Context Divide & Conquer: Break massive tasks (e.g., researching 10 companies) into isolated windows to bypass context window limits.

Physical Context Separation: Move beyond "Role Playing" to true "Context Isolation," ensuring no single agent is overwhelmed by noise.

The Manager Agent Workflow: Build a hierarchical structure where sub-agents execute deep work and a Manager Agent synthesizes strategy.

Define Success First: Unlearn "Fixing." Force yourself to write clear Acceptance Criteria/Test Cases before the AI writes a single line.

The 5/95 Split: Shift your energy to the 5% of "Definition" and delegate the 95% of "Execution" to the AI, escaping the execution trap.

Probabilistic Management: Learn to manage AI not as a calculator, but as a probabilistic employee, focusing on risk control and guidance.

Automated Measurement Sets: Use AI to generate its own test cases (e.g., error types) to stress-test your prompts against reality.

Instant Dashboards: Build React-based visualization tools in minutes to see error rates (like Omission %) instead of relying on vibe checks

Metric-Driven Decisions: Move from qualitative feelings to quantitative metrics (e.g., Levenshtein distance) to judge AI performance.

Closed Feedback Loops: Architect systems where AI perceives its own output errors and auto-corrects code without human intervention.

Second-Order Leverage: Instruct AI to analyze its own failure patterns (Coaching) and rewrite its own V2 prompts (Optimization).

The Data Flywheel: Design workflows that turn user corrections into ground-truth data, creating a system that gets smarter with every use.