Prompt Engineering Guides

AI Improvement Prompting for Operators

Take rough drafts, vague tickets, and messy notes and refine them into polished, client-ready deliverables with structured improvement prompts.

Building an Improvement Prompt Step by Step

Walk through every piece of an improvement prompt: role, task, requirements, tone, output format, and real examples from your backlog.

Improvement Prompt Templates for Client Work

Three ready-to-use improvement prompts for client reports, proposals, and meeting notes. Plus the backlog trick and chaining pattern.

Markdown Prompting: Structure Your AI Inputs

Why formatting your prompts as Markdown improves AI output quality, and how to structure prompts with headers, lists, and code blocks.

Markdown Prompt Templates for Operators

Copy-paste Markdown prompt templates for client reports, content editing, and meeting prep. Plus common formatting mistakes to avoid.

XML Prompting: Structured Output for Complex Tasks

When and why to deploy XML as an AI output format. Covers tag structure, CDATA wrapping, and the code changes prompt pattern.

XML Prompt Templates and Patterns

Ready-to-use XML prompt templates for project tracking, change logs, and process documentation. Plus parsing and validation guidance.

JSON Output Prompting for Operators

How to get AI models to produce valid, parseable JSON. Covers schema definition, the array wrapper pattern, and preventing parser-breaking output.

JSON Prompt Templates for Client Work

Three JSON prompt templates for meeting extraction, competitive analysis, and engagement tracking. Plus validation and workflow chaining.

Prompt Engineering: The Absolute Basics

What prompting is, how tokens work, context windows, temperature, and pricing. The foundation every operator needs before writing a single prompt.

AI Chat Apps for Prompt Engineering

How ChatGPT, Claude, and Gemini work under the hood. Why hidden system prompts affect your results and when to use chat apps vs playgrounds.

AI Playgrounds for Prompt Engineering

OpenAI Playground, Anthropic Workbench, and Google AI Studio. Direct model access, temperature control, and proper A/B testing for your prompts.

AI Prompt Generators for Operators

How prompt generators turn a rough description into a structured prompt. When to use them vs writing prompts yourself.

AI Prompt Improvers: Refine What You Already Wrote

How prompt improvers take an existing prompt and make it better. The difference between generators and improvers, and when to use each.

The Door Rule: A Mental Model for Prompting

Imagine sliding instructions under a door. If the person on the other side can not complete the task, your prompt needs more context.

Common Prompting Tips That Actually Work

Battle-tested prompting patterns for operators. Be direct, front-load instructions, provide context, and build feedback loops.

The Anatomy of a Good Prompt

The seven components of an effective prompt: role, task, context, details, format, examples, and tone. With annotated examples for every piece.

Using Different AI Models Effectively

Three tiers of models (fast, flagship, reasoning) across three labs. Which model to pick for which deliverable and how to switch mid-workflow.

Reasoning Models: When Thinking Time Pays Off

How reasoning models differ from regular models, why they need less instruction, and when the extra cost and latency are worth it.

Case Studies: Start Here

How to use the prompt engineering case studies. Maps each case study to the operator tasks it solves.

Summarization Prompting for Operators

How to build AI prompts that summarize reports, meetings, and long documents into structured, client-ready outputs.

Simplification Prompting: Translate Complexity for Any Audience

How to prompt AI to simplify technical content for boards, end users, and new hires without losing accuracy.

Prompt Engineering Resources Worth Reading Before You Start

Four resources from OpenAI and Anthropic that give operators grounding before the hands-on work begins. What each covers and when to read it.