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We help get usable results out of AI instead of vague ones. Repeatable patterns, worked before-and-after examples, and a framework you can apply in sixty seconds to anything that matters.

C-R-A-F-T: Five Parts of an effective AI Prompt
Most bad AI output traces back to a prompt that was missing something the model had no way to supply on its own.
Not missing a clever phrase. Missing information. The reader it was written for. The constraint it had to respect. The format it needed to arrive in. People type a nine-word instruction, receive nine words' worth of usefulness, and conclude the tool is overrated.
C-R-A-F-T is a checklist for the sixty seconds before you hit send. Context, Role, Action, Format, Test. You will not need all five every time. For anything that matters, running through them roughly doubles the quality of what comes back, and it costs less time than rewriting the answer.
C: Context The situation, the background, the facts that bear on this.
This is the part people skip most and it is the part that matters most, because the model knows nothing about your world. It has never seen your product, your team, your customers, or last quarter's results. Every relevant detail you leave out is a gap, and gaps do not come back as questions. They come back as confident invention.
Weak: "Write an update about the project delay." Better: "Our integration is slipping two weeks because a vendor dependency landed late. This is a long-standing enterprise client and it is our first delay with them."
Same task. The second version has something to work with.
A useful instinct: give more context than feels necessary. It is nearly impossible to over-explain background to these systems. It is very easy to under-explain it.
R: Role The perspective the model should take.
This works because the model learned patterns from an enormous range of writing, and naming a role steers which patterns it draws on. Text written by a financial controller genuinely does differ from text written by a marketer, and the model absorbed that difference.
The trick is specificity. "Act as a finance expert" is nearly useless, because it names a category rather than a person with a viewpoint. "Take the role of a controller at a mid-size manufacturer who has been burned before by optimistic revenue forecasts" gives the model something to actually do.
Roles are most valuable when you want a particular kind of scrutiny. A skeptical buyer reading your pricing page will find different problems than a generic request to review it.
A: Action The exact task, stated as a verb.
Vague asks produce vague output, and this is where most prompts quietly fail. "Look at this" and "help me with this" do not specify an operation. Summarise, rank, compare, rewrite, extract, critique, translate, categorise: each of these is a different job with a different result.
Be specific about scope too. "Identify the three most likely causes" produces something usable. "Analyse the situation" produces an essay.
If you want more than one thing done, do them in separate messages. Bundled requests dilute every part of the answer. The model spreads its effort across all of them and does none particularly well.
F: Format Structure, length, tone.
The model has no default sense of what you will do with the output. A table, five bullets, two paragraphs, a formal letter, a Slack message: all are equally available, and if you do not choose, you get whatever the request's phrasing suggested, which is usually an essay.
State the shape. "A table with columns for driver, evidence, and confirming test. Under 200 words." Naming a word limit does more work than people expect, because it forces prioritisation. A constrained answer has to lead with what matters.
T: Test The constraints and success criteria the answer has to satisfy.
This is the least used and, for anything with real stakes, the most valuable. You are telling the model what would make the output wrong.
"Use only the information in the notes below. Do not guess. If something is not covered, write 'not stated' rather than filling it in."
"Flag anything you are uncertain about rather than smoothing over it."
"Do not offer legal advice. Where an issue needs a lawyer, say so."
These instructions genuinely change the output. They will not eliminate errors, and you still have to check, but you are substantially narrowing the space in which the model invents things.
All five, on one request Take a real example. Renewal rate dropped from 91 percent to 84 percent, and you have the exit survey notes.
Context: "Our Q3 renewal rate fell from 91% to 84%. The exit survey notes are included below."
Role:&nbsp;"Take on the role of a customer success director."
Action: "Identify the three most probable drivers, and specify what evidence would confirm each one."
Format: "A table with columns for driver, supporting evidence, and confirming test. Keep it under 200 words."
Test: "Rely only on the provided notes. Do not guess, and flag anything the notes do not back up."
Roughly ninety words of setup. What comes back is an analysis you can take into a meeting.
Compare it to "why did our churn go up," which produces a competent general essay about customer churn that would apply equally to any company on earth.
When to skip parts Role is often unnecessary for mechanical tasks. Reformatting a list does not need a persona.
Test matters most when the model might fill gaps, which is to say whenever it is working with your data or your documents.
Context is the one to skip least. If you only have time for one element, give it the background.
The thing nobody mentions The real benefit of running through C-R-A-F-T is not what it does to the model. It is what it does to you.
Half the time, working through the five parts reveals that you had not decided what you wanted. You did not know who the document was for, or what decision it needed to support, or how long it should be. You were going to find out by writing it.
Sixty seconds of structure surfaces that before you have generated three pages in the wrong direction. Which is useful on its own, whether or not you ever open the tool.

Everything AI - beyond the hype

Six Rules of Thumb Worth Following
Watch someone who is genuinely good with these tools and the difference is not a secret prompt. It is a handful of habits, applied consistently.
Here are six that carry most of the weight.
Be specific "Improve this" gets you vague noise. "Cut this to 150 words and keep the deadline in the first sentence" gets you something usable.
Vagueness in, vagueness out. The model cannot ask what you meant, so it picks an interpretation and commits. When the result is wrong, the cause is usually that you left the choice open and it chose differently than you would have.
Specificity costs one extra sentence and saves an entire round of correction.
Show, do not just describe One example of a good result beats three paragraphs explaining what good means.
If you have a past document written the way you want this one written, paste it in. If you have a row of a table formatted correctly, include it. Quality standards are hard to articulate and easy to demonstrate, and the model is very good at matching a pattern it can see.
This applies to tone above all. Describing a tone as "professional but warm" leaves enormous room. Showing a paragraph in that tone closes it.
One task at a time Bundled requests weaken every part of the answer.
Asking it to summarise a document, identify risks, and draft a response in one message produces three mediocre outputs. The same three requests sent separately produce three good ones, and each can build on the last.
There is a reason for this beyond attention. Each answer becomes context for the next request, so sequencing gives you compounding quality. Bundling throws that away.
Ask for options "Give me three approaches" reveals tradeoffs that a single answer hides.
When you ask for one recommendation, you get one, delivered with the same confidence regardless of whether it was a clear winner or an arbitrary pick among equals. You learn nothing about the decision space.
Three options force the differences into view. Often you will take pieces of two of them, which is a better outcome than either alone, and you would never have got there from a single answer.
If you are stuck, restart A conversation that has gone off track rarely gets back on.
This one surprises people, because it runs against the instinct to keep correcting. But everything said so far stays in view, including the wrong turn and your three attempts to fix it. All of it shapes what comes next. You are pulling against accumulated weight.
Open a new conversation and write a sharper opening prompt, using what you learned from the failed attempt. It is faster almost every time. Two rounds of correction is a reasonable limit before starting over.
Never send anything you have not read Every word of it. Start to finish.
This is the rule that protects the other five, and it is the one people abandon first, usually when they are busy and the output looks fine at a glance.
Output that looks fine at a glance is the specific failure mode of this technology. It is built to produce text that reads well. A fabricated figure sits in a sentence looking exactly like a real one. The only reliable defence is a person who reads the whole thing and knows the subject.
If your name goes on it, you wrote it. Whatever produced the first draft.
The pattern underneath Five of these six are about being clear before you ask. The sixth is about being careful after.
That is roughly the whole discipline. The model is not the variable you control. Your input and your review are.
People who struggle with these tools are usually giving thin instructions and skimming the results. People who get real value are specific going in and thorough coming out. The gap between those two groups has almost nothing to do with which tool they are using.

Before and After, Example: Summarizing a Report 
The weak prompt
"Summarize this report."
 The strong prompt
"Summarize the attached report for a VP with only 90 seconds to spare. No more than five bullets. Open with the decision that is needed, then the supporting evidence. Call out anywhere the report's own data seems to contradict itself." What changed
Four things. - 
<ul>
<li>The reader is named, and given a time budget.</li>
<li>A length limit is set.</li>
<li>The order of priority is specified, decision first.</li>
<li>A critical read is requested, not just a condensation.</li>
</ul>
Why each one matters
The reader. "Summarize" has no natural audience, so the model writes for nobody in particular, which in practice means it writes for a patient generalist. A VP with ninety seconds is a completely different job. It changes what gets cut.
The length. Five bullets forces ranking. Without a limit the model tends to preserve the document's own proportions, giving equal weight to the important section and the procedural one. A cap makes it choose.
The order. Most summaries reproduce the source's structure, which usually buries the decision at the end where the original author put it. Saying "open with the decision that is needed" inverts that. Your reader gets the point in sentence one.
The critical read. This is the addition that does the most work and gets used the least. You are asking the model to do something a summary does not normally do: look for internal inconsistency. Reports contradict themselves more often than people expect, usually because different sections were written by different people from different data pulls. The model is good at catching this, and will not do it unless asked.
One variation to try
Add a second pass on your own work:
"Now list the three things a skeptical reader would push back on first." Run it on your own report before you circulate it. What comes back is a reasonable approximation of the questions you will actually get, and you have the chance to answer them in the document instead of in the room.
The underlying move
The weak version asks for a task. The strong version specifies a reader, a constraint, a priority order, and a standard.
None of that is clever phrasing. It is just deciding what you want before you ask, which is most of what separates a usable AI output from a generic one.

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