The Learning Prompt Engineering Blog
Practical articles on how AI models process prompts, methods that often improve your results, and how those techniques hold up in real tasks. Organized into three areas — getting started, techniques, and real-world use — so you can start wherever’s useful to you.
What's New
Fresh content across all three topic areas — published on a rolling schedule.
Prompting Claude — What to Expect and How to Adjust
A practical guide to Claude’s default output tendencies — where they help, where they create friction, and how to adjust your prompts to get the output you need.
3 AI Model Constraints Every Prompt Writer Should Understand
Covers the three key constraints that shape every AI interaction — context limits, training data boundaries, and the absence of real-time data access in base models — with practical implications for each.
What Happens When You Send a Prompt to an AI Model
xplains what actually occurs between the moment you submit a prompt and the moment a response appears, covering tokenization, context processing, and output generation.
Tokenization Explained How AI Models Break Down Your Text
Covers the tokenization process — how AI models split prompts into smaller units — and why word choice and phrasing structure affect how input is interpreted.
Base AI Models vs Tool-Connected AI Systems What’s the Difference
Explains the key distinction between base AI models — text-only, no tools, no memory — and tool-connected AI systems, and why this distinction matters when designing prompts.
Why Specific Prompts Tend to Produce Better Results
Uses side-by-side examples to show how adding format, context, and tone to a prompt narrows the output range and typically produces more useful responses.
Getting Started
New to prompt engineering? This is where you begin. You'll learn what prompts actually are, how AI models process them, and the foundational concepts that make everything else click.
Prompting Claude — What to Expect and How to Adjust
A practical guide to Claude’s default output tendencies — where they help, where they create friction, and how to adjust your prompts to get the output you need.
3 AI Model Constraints Every Prompt Writer Should Understand
Covers the three key constraints that shape every AI interaction — context limits, training data boundaries, and the absence of real-time data access in base models — with practical implications for each.
What Happens When You Send a Prompt to an AI Model
xplains what actually occurs between the moment you submit a prompt and the moment a response appears, covering tokenization, context processing, and output generation.
Tokenization Explained How AI Models Break Down Your Text
Covers the tokenization process — how AI models split prompts into smaller units — and why word choice and phrasing structure affect how input is interpreted.
Base AI Models vs Tool-Connected AI Systems What’s the Difference
Explains the key distinction between base AI models — text-only, no tools, no memory — and tool-connected AI systems, and why this distinction matters when designing prompts.
Why Specific Prompts Tend to Produce Better Results
Uses side-by-side examples to show how adding format, context, and tone to a prompt narrows the output range and typically produces more useful responses.
Prompt Techniques
Practical methods for writing better prompts. Zero-shot, few-shot, the seven-element framework, iterative refinement, A/B testing — each one explained clearly with examples you can apply right away.
Prompting Claude — What to Expect and How to Adjust
A practical guide to Claude’s default output tendencies — where they help, where they create friction, and how to adjust your prompts to get the output you need.
3 AI Model Constraints Every Prompt Writer Should Understand
Covers the three key constraints that shape every AI interaction — context limits, training data boundaries, and the absence of real-time data access in base models — with practical implications for each.
What Happens When You Send a Prompt to an AI Model
xplains what actually occurs between the moment you submit a prompt and the moment a response appears, covering tokenization, context processing, and output generation.
Tokenization Explained How AI Models Break Down Your Text
Covers the tokenization process — how AI models split prompts into smaller units — and why word choice and phrasing structure affect how input is interpreted.
Base AI Models vs Tool-Connected AI Systems What’s the Difference
Explains the key distinction between base AI models — text-only, no tools, no memory — and tool-connected AI systems, and why this distinction matters when designing prompts.
Why Specific Prompts Tend to Produce Better Results
Uses side-by-side examples to show how adding format, context, and tone to a prompt narrows the output range and typically produces more useful responses.
Real World Use
Prompt engineering applied to real work — content creation, business automation, professional workflows, and visual tools. The focus here is practical: what to prompt, how to structure it, and what to do when the output needs work.
Prompting Claude — What to Expect and How to Adjust
A practical guide to Claude’s default output tendencies — where they help, where they create friction, and how to adjust your prompts to get the output you need.
3 AI Model Constraints Every Prompt Writer Should Understand
Covers the three key constraints that shape every AI interaction — context limits, training data boundaries, and the absence of real-time data access in base models — with practical implications for each.
What Happens When You Send a Prompt to an AI Model
xplains what actually occurs between the moment you submit a prompt and the moment a response appears, covering tokenization, context processing, and output generation.
Tokenization Explained How AI Models Break Down Your Text
Covers the tokenization process — how AI models split prompts into smaller units — and why word choice and phrasing structure affect how input is interpreted.
Base AI Models vs Tool-Connected AI Systems What’s the Difference
Explains the key distinction between base AI models — text-only, no tools, no memory — and tool-connected AI systems, and why this distinction matters when designing prompts.
Why Specific Prompts Tend to Produce Better Results
Uses side-by-side examples to show how adding format, context, and tone to a prompt narrows the output range and typically produces more useful responses.