Prompt Engineering

26 stories tagged Prompt Engineering on Learning Fanatic.

Lesson 3.2: Writing instructions and system prompts for agents Learn to write agent instructions that work: clear structure, specific rules with reasons, the right place for each rule and tests that tell versions apart.
Lesson 1.1: What a prompt is and how a model uses it Learn what a prompt really is, what a language model can and cannot know, and why a sharper prompt saves you time on every task you give it.
Lesson 2.1: Examples: zero, one and few-shot prompting Learn when examples beat instructions, how to choose and format them, and how to tell whether another example is worth its cost.
Lesson 1.3: The anatomy of a good prompt: task, context, format and more Learn the six building blocks of a strong prompt, how to use role and delimiters well, and how to turn a good prompt into a reusable template.
Lesson 1.4: Clear instructions: vague against sharp Learn to turn vague requests into instructions a model can follow and you can check, and why too many rules lower the chance that every one is followed.
Lesson 2.4: Checking the output: spotting hallucinations Learn why models state false things fluently, which claims carry the most risk, and a practical routine for checking facts, citations, numbers and summaries.
Lesson 2.2: Context, files and reference material Learn how to give a model the facts it needs, tell it how to use them, prepare files and spreadsheets, and make answers easy to verify.
Lesson 3.1: Step-by-step reasoning, and when it helps Learn when asking a model to show its steps improves answers, how to prompt for reasoning, how voting across runs helps, and what it costs.
Lesson 4.1: Prompts for writing and editing at work Learn prompts for drafting, editing and reshaping work writing, from the four levels of edit to cutting a long text, while keeping your facts and voice.
Lesson 4.2: Research, summarising and extraction prompts Learn prompts for researching a topic, summarising faithfully and pulling structured facts from documents, with a sampling check for large batches.
Lesson 4.3: Analysis and data work with a model Learn to use an assistant for analysis without being misled by its arithmetic: describe your data, ask for the method first, verify sums and test formulas.
Lesson 5.3: Grounding, citations and long documents Learn to tie answers to supplied text with checkable citations, test what a prompt does when the answer is missing, and handle very long documents.
Lesson 5.4: Structured outputs, tools and agent instructions Learn to get output that software can read, describe tools so a model uses them correctly, and write agent instructions with limits, checks and approval points.
Lesson 6.1: Making results consistent from run to run Learn why one prompt gives different answers, how to measure the variation, and the levers and regression checks that keep results steady.
Lesson 6.2: Prompt injection and untrusted input Learn how text a model reads can carry hidden orders, which setups put you at risk, and the layered defences and tests that limit the damage.
Lesson 6.3: Privacy, bias and responsible use of AI at work Learn what data not to paste, how to test a prompt for unequal treatment, and a practical rulebook for human oversight, disclosure and accountability.
Lesson 6.4: Capstone: build and test your own prompt library Bring the course together in one project: choose your five highest-value tasks, build tested, safe prompts for each, document them and measure what they save.
Lesson 1.2: Tokens, context and why wording matters Learn what tokens and context windows are, how to budget the text you send, and how word choice and order quietly change what a model gives back.
Lesson 2.3: Controlling format, tone and reading level Learn to control the length, structure, voice and reading level of a reply so it is ready to use, with a readability formula and a reusable style sheet.
Lesson 3.2: Breaking a task into steps with prompt chaining Learn to split a big request into a chain of small prompts, pass results between them, place human checkpoints and judge when a chain beats one prompt.
Lesson 3.3: System prompts, custom instructions and reusable templates Learn to write standing instructions that shape every reply, build a tested template library, version it, and share it across a team without drift.
Lesson 3.4: Self-checks, revision and improving your own prompt Learn to run a draft, critique and revise loop with a rubric, know what a model's self-review can and cannot catch, and use it to improve your prompts.
Lesson 4.4: Planning, operations and code help prompts Learn prompts for project plans, risks, procedures and small scripts, with a critical-path example and safe habits for running code you did not write.
Lesson 5.1: Testing and comparing prompts with rubrics and test sets Learn to build a small test set, score outputs with a rubric, use a model as a grader carefully, and tell whether one prompt really beats another.