Prompt engineering guides

Less magic wording, more verifiable working methods. Each guide focuses on real tasks, constraints, and quality checks.

Prompt fundamentals

How to Write an AI Prompt That Actually Works

A practical framework for turning vague requests into precise, testable prompts for ChatGPT, Claude, Gemini, and other AI tools.

AI coding

A spec-driven method for getting safer, reviewable code changes from Cursor and other AI coding assistants.

Model workflows

Keep one model-neutral task specification and adapt context, tools, and output contracts for different AI assistants.

Prompt evaluation

Build a small, repeatable prompt evaluation using representative cases, explicit rubrics, failure categories, and cost-aware decisions.

Team workflows

Move from one-off prompting to reusable briefs, approved source packs, review gates, and measured production workflows.

SaaS marketing

A practical workflow for turning approved product facts, customer evidence, and campaign goals into reviewable SaaS marketing drafts.

AI coding

A preflight checklist for giving Cursor, Copilot, and other coding assistants enough repository context, scope, and verification requirements.

Prompt operations

Organize a prompt library around repeatable jobs, source boundaries, variables, examples, owners, and evaluation evidence.

Prompt fundamentals

A question-driven method for clarifying goals, evidence, constraints, output contracts, and acceptance tests before using AI.

Tool selection

Compare prompt optimizers and template libraries by task fit, customization cost, repeatability, risk, and team workflow.