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This path takes a true beginner from “I asked… it guessed” to “I ask clearly and get dependable results.” You’ll learn why language models follow structure more than intent, how to give them the right context, and how to lock the shape of outputs so they’re paste-ready. Along the way you’ll practice quick checks that reduce hallucinations, add gentle reasoning, and shape tone without losing accuracy. The last modules introduce multimodal basics and a tiny step into retrieval so you can ground answers in your own material. Everything is hands-on, narrative-first, and easy to reuse at work.
Prompt engineering is the skill of shaping inputs so LLMs like ChatGPT deliver clear, accurate, and useful results. This guide walks you through the foundations, core techniques, and practical strategies to help you design prompts that truly work.
Beginner’s guide to system vs. user prompts. Learn how separating policy (voice, structure, boundaries) from task cuts drift, boosts reliability, and enables reusable policies.
This guide shows how to write prompts that models can’t misunderstand. Learn the ARCF framework—Ask, Role, Constraints, Format—to get reliable outputs in bullets, steps, tables, or JSON. Includes acceptance criteria, reusable system prompts, common fixes, and practical examples so you can reduce drift, cut fluff, and hit the exact format you need every time.
Learn how to give models the right context for better results. Understand context windows, when to ground vs. rely on knowledge, and how to package inputs with delimiters. Includes scaffolds, acceptance criteria, a mini lab, and troubleshooting tips.
Learn to make LLMs return schema-matching JSON. Write a minimal JSON Schema, constrain outputs with a system prompt, and auto-validate every response with a repair loop. Includes a hands-on lab to build, test, and confirm valid outputs.
"This guide shows how to use self check prompts that ask LLMs to rate confidence from 1 to 5 with a short reason. You will learn why showing uncertainty matters, how to anchor the scale, and practice in a lab to build trust and improve reliability.
Beginner’s guide to reducing LLM hallucinations. Learn to spot weak answers, add source checks, and use confidence fields. Includes a lab on adding a verify-before-answering step to boost reliability.
"This beginner guide explains evaluation loops for prompts. You will build a golden set of test cases, create a pass or fail rubric, and log results. A hands-on lab shows how to compare prompt variants, record outcomes, and turn failures into improvements.
Beginner’s guide to few-shot prompting. Learn zero-shot vs. few-shot, how to choose crisp examples, and test both on a labeling task. Includes a reusable template for reliable labels and JSON outputs.
This guide shows how to design roleplay prompts with Role Cards that define role, stance, mandate, evidence rules, and format. With examples, scaffolds, and a lab, you learn how role choice shapes tone, cuts fluff, prevents errors, and makes outputs useful.
Advanced-beginner guide to Chain-of-Thought Lite. Use a simple Plan → Work → Answer scaffold to cut mistakes, boost accuracy, and get clearer outputs. Includes a reusable system prompt, task templates, and a mini lab on stepwise reasoning.
Learn how to write summaries that keep key names, dates, places, and numbers. Explore Chain of Density, a prompt pattern that replaces vague text with grounded entities. Includes prompts, examples, troubleshooting, and a mini lab on real articles.
Learn Retrieval Augmented Generation for small document sets using a clear librarian and writer model. Build a minimal RAG workflow from chunking to citations, connect LLMs to retrievers, format grounded prompts, and test answers in a hands-on mini lab.
This advanced guide teaches how to make LLM outputs polished and consistent. You will build a style guide in the system prompt, add a verifier checklist, and test style variants. Labs cover rewrite scaffolds, length control, style tokens, and polishing tactics.
Learn multimodal prompting by putting images first, designing a clear extraction schema, and adding guardrails like locale, units, and uncertainty. Practice with a receipt photo, compare against prose, and pick up practical tips for reliable JSON outputs.
Learn consistent AI image generation with JSON prompting. Lock identity and style, vary scenes without drift, and build stable positive and negative prompt sets for reliable, repeatable visuals.
This beginner guide to instruction tuning uses short feedback loops to refine prompts step by step. You will practice improving a blog post prompt with audience, tone, length, and structure, then compare drafts using a rubric and self checks.
- Basic computer literacy and comfort writing short English paragraphs. - Access to a modern LLM chat interface (any vendor). - Willingness to copy/paste small snippets and test them. - A few sample texts you care about (e.g., an article or meeting notes).
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