Using AI to Outline Before You Write, Not Just to Write
Asking for a full draft first often produces something you have to fight against — an outline first gives you more control over the direction.
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Asking for a full draft first often produces something you have to fight against — an outline first gives you more control over the direction.
By default, most models guess at what you meant rather than asking — you can change that with one explicit instruction.
Models default toward thorough, padded answers unless told otherwise — a few specific instructions reliably fix that.
The same request produces very different output depending on who it's for — naming the audience is one of the highest-leverage things you can add to a prompt.
The default brainstorm from an AI model tends to be safe and generic — a few prompting adjustments push it toward more genuinely useful ideas.
Getting clean, reliable data out of unstructured text — invoices, emails, reviews — comes down to a few specific prompting habits.
No single AI tool is best at everything — chaining a few together for what each does best often beats forcing one tool to do it all.
When a response goes wrong, working through a few likely causes usually finds the fix faster than rewriting the whole prompt from scratch.
A model can often catch its own mistakes if you simply ask it to check its work before you review it — a cheap step that saves real editing time.
A simple formatting habit — wrapping pasted content in clear markers — prevents a model from confusing your instructions with the text you gave it.
The same model needs to be prompted quite differently depending on whether you want precision or variety.
A handful of habits account for most disappointing AI responses — here are the ones worth fixing first.
Generic prompts get generic code — a few specific details make a real difference in how usable the output actually is.
Formatting drifts from response to response unless you're specific about it — here's how to lock it down.
Pasting an entire document in and hoping for the best is the least reliable way to get a good summary — a little structure goes a long way.
Telling a model what to avoid is sometimes more effective than describing what you want — here's when and how to use it.
The fundamentals of writing prompts that get you useful answers instead of vague, generic ones — no special tools required.
The flagship, most expensive model isn't automatically the right pick — smaller, faster models win for a lot of everyday tasks.
Confident, fluent, and wrong is the most dangerous failure mode of any language model. Here's why it happens and how to guard against it.
Fine-tuning sounds more powerful, but for most use cases a well-written prompt gets you further, faster, and cheaper.
RAG is how AI products answer questions about your own documents or data without retraining the underlying model.
That temperature slider in AI tool settings does something specific and useful once you know what it actually controls.
Every model has a hard limit on how much text it can "see" at once — here's what that means in practice and how to work around it.
Most chat AI tools give you two channels for instructions — understanding the difference makes both far more useful.
Asking a model to "think step by step" before answering measurably improves accuracy on anything that involves multi-step reasoning.
Instead of describing what you want, show a couple of examples — it's often the fastest way to get consistent, on-format answers.
The fundamentals of writing prompts that get you useful answers instead of vague, generic ones — no special tools required.
The flagship, most expensive model isn't automatically the right pick — smaller, faster models win for a lot of everyday tasks.
RAG is how AI products answer questions about your own documents or data without retraining the underlying model.
Every model has a hard limit on how much text it can "see" at once — here's what that means in practice and how to work around it.
Most chat AI tools give you two channels for instructions — understanding the difference makes both far more useful.
Asking a model to "think step by step" before answering measurably improves accuracy on anything that involves multi-step reasoning.
Instead of describing what you want, show a couple of examples — it's often the fastest way to get consistent, on-format answers.
The fundamentals of writing prompts that get you useful answers instead of vague, generic ones — no special tools required.
The flagship, most expensive model isn't automatically the right pick — smaller, faster models win for a lot of everyday tasks.
Confident, fluent, and wrong is the most dangerous failure mode of any language model. Here's why it happens and how to guard against it.
Fine-tuning sounds more powerful, but for most use cases a well-written prompt gets you further, faster, and cheaper.
RAG is how AI products answer questions about your own documents or data without retraining the underlying model.
Most people use ChatGPT all wrong. Here are 10 simple techniques to get dramatically better results from any AI chatbot.