Guides
Practical articles on every skill this program covers. Free to read, written to actually help.
Chain of thought prompting: how to get reliable stepwise AI reasoning
Learn how to ask AI for stepwise reasoning so you get clearer answers, catch mistakes faster and solve complex tasks more reliably.
Read the guide →How to write clear AI instructions that get the result you want
Learn how to give AI concise, unambiguous instructions so it understands your task and produces useful results for real everyday problems.
Read the guide →Context windows and token limits: why AI responses get cut off
Learn how context windows and token limits work so you avoid cut off answers, missing details and broken workflows when using AI models.
Read the guide →Why LLM evaluation with clear test cases matters
Learn how simple, well chosen test cases help you judge LLM answers, avoid surprises and steadily improve your prompts in real everyday tasks.
Read the guide →Few-shot prompting with examples: how to guide AI output
Learn how to steer AI tools with a few concrete examples. Use few-shot prompting to get more reliable, on-style answers for your real tasks.
Read the guide →Foundations of prompt engineering for better everyday AI results
Learn core LLM concepts, prompt patterns, testing, and safety so your everyday AI prompts become clearer, more reliable, and easier to reuse.
Read the guide →Hallucination mitigation strategies for safer AI prompts
Learn simple prompt and workflow habits that cut AI hallucinations, so your outputs stay closer to real, checkable facts.
Read the guide →LLM failure modes explained and how to spot common problems
Learn to recognize LLM failure modes like hallucinations, prompt injection and instruction drift so your AI outputs stay safe and reliable.
Read the guide →How to use LLMs in your workflow and plan an AI career
Learn how to plug LLMs into your daily work, avoid common traps and turn growing prompt skills into real career options.
Read the guide →Mental model of LLMs: how they think and why they err
Build a simple mental model of how LLMs behave so their odd answers feel predictable and you can design prompts that work reliably.
Read the guide →How to specify AI output format for clean, usable results
Learn how to ask AI for bullet lists, tables or JSON so outputs are structured, consistent and ready to paste into your real everyday workflows.
Read the guide →How probabilistic text generation in LLMs really works
Learn how large language models predict the next token from probabilities, why this causes mistakes, and how to write prompts that work with it.
Read the guide →Prompt as input specification: how to tell an AI exactly what you need
Learn to treat prompts as clear input specifications, so AI tools follow your task, constraints and context with fewer mistakes.
Read the guide →Prompt injection and guardrails: how to keep AI outputs safe
Learn what prompt injection is, why it is risky for real data and workflows, and how to design simple guardrails that block common attacks.
Read the guide →Prompt iteration and refinement: how to improve AI prompts fast
Learn a simple loop to test, tweak and refine your AI prompts so outputs get clearer, more accurate and more useful with each try.
Read the guide →How to turn good AI prompts into reusable templates
Learn how to turn one good AI prompt into a simple template with variables you can reuse in minutes for work, study and daily tasks.
Read the guide →How to build simple prompt testing workflows that actually work
Learn how to test AI prompts on small, realistic case sets so you can trust results, fix weak spots fast and avoid hidden failures.
Read the guide →Role conditioning in prompts: how to set the right expert voice
Learn how to use role statements in prompts so AI answers like a specific expert, teacher or assistant that fits your real-world task.
Read the guide →Temperature and randomness in AI: how to get stable or creative replies
Learn how temperature and related settings shape AI output: more creativity or more reliability, and how to tune them for everyday tasks.
Read the guide →Tokenization basics: how LLMs count text and why it matters
Learn how text is split into tokens, how models count them, and how this affects costs, limits and the quality of your prompts.
Read the guide →Turn reading into real progress
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