Generative AI has graduated from research labs to everyday workflows, drafting blog posts, illustrating storyboards, writing code, and even composing music. Yet the distance between an under-powered response and a dazzling, production-ready result is almost never the model itself—it is the prompt. Think of a prompt as the blueprint for a building: if the plan is vague, the construction crew will improvise; if the plan is precise, the structure stands tall on the first try. Master the seven rules below and you will consistently coax high-quality output from large language models (LLMs), text-to-image tools, or any other generative system you encounter.
1. Lead With Context
Why it matters
Language models predict text based on the information you provide. Without context, they must guess your industry, your audience, and your goals—often badly. With context, they can align tone, jargon, and complexity to exactly what you need.
How to do it
Start every prompt with two or three lines that answer the questions who, what, and why. Mention your organization, the audience’s knowledge level, and the outcome you are chasing.
Example
“We are a fintech consultancy preparing educational LinkedIn threads for first-time investors. Write a post that simplifies the concept of compound interest while encouraging readers to download our budgeting app.”
Pro tip
If the task continues over multiple messages, periodically restate the context so the model “remembers” after token limits push older text out of scope.
2. Assign the AI a Role
Why it matters
Humans speak differently as a doctor, a teacher, or a stand-up comedian. LLMs emulate that same spectrum when you explicitly tell them who they are supposed to be.
How to do it
Use phrases such as “Act as…”, “You are…”, or “Pretend you are…” to load the model with a persona.
Example
“Act as an award-winning outdoor advertising copywriter who specializes in sports brands and short motivational slogans.”
Pro tip
Roles stack. You can ask: “Act as a B2B SaaS marketer and an accessibility consultant” to ensure both brand persuasion and compliance-friendly language.
3. Make the Core Instruction Clear—and Measurable
Why it matters
Generative models happily follow the path of least resistance. A fuzzy instruction like “Help me with this” yields fuzzy content. A precise, criteria-driven instruction forces the model to focus.
How to do it
Define one primary verb (write, design, translate, diagram), the format (list, tweet thread, HTML email), and the limits (word count, time length, resolution).
Example
“Write three billboard headlines, each no longer than eight words, suitable for a 6 × 3-meter poster.”
Pro tip
If you need multiple deliverables—say, five subject lines AND a 200-word body—break them into numbered tasks. Models respect hierarchy when you hand it to them.
4. Spell Out Technical Parameters and Hard Constraints
Why it matters
Brand colors, reading-level scores, forbidden phrases, alt-text requirements—these are not suggestions; they are non-negotiables. If they are missing, the AI will produce a generic answer that may violate your guidelines.
How to do it
List constraints in the same prompt, preferably as bullet-style sentences starting with “Must…” or “Do not…”.
Example
• Do not use the word “discount.”
• Must maintain a Flesch Reading Ease score of 60+.
• Must apply the color palette #14213D / #FCA311 / #E5E5E5 in any visual references.
Pro tip
Treat constraints as version-controlled assets. Keep a living style-guide snippet you can copy-paste into any new prompt.
5. Provide Examples (Few-Shot Learning)
Why it matters
LLMs learn patterns on the fly when you show them samples. A single paragraph in your target style often steers output better than a page of adjectives like “professional yet playful.”
How to do it
Paste two or three short exemplars—text passages, image links, or code snippets—then instruct the model to match tone or structure.
Example
“Here is a caption we love: ‘Money is the passport to your next adventure.’ Write five new captions with a similar rhythm, each focusing on eco-friendly investing.”
Pro tip
Label your examples with comments such as <EXAMPLE START> and <EXAMPLE END> so the model can distinguish them from instructions.
6. Declare Success Criteria Up Front
Why it matters
If you do not know how you will judge success, neither can the AI. Setting measurable acceptance criteria pushes the model to self-evaluate and prevents “looks good to me” complacency.
How to do it
End your prompt with bullets that list every box the output must tick.
Example
The output is acceptable when:
- Every sentence begins with a verb.
- At least one explicit CTA appears.
- Total length does not exceed 120 words.
Pro tip
Pair criteria with scoring rubrics: “Rate your own draft 1–10 on clarity, concision, originality, then rewrite anything < 8.” The model will often revise and resubmit in one sweep.
7. Run a Generate → Critique → Refine Loop
Why it matters
Even the best initial prompt rarely hits perfection on the first pass. An iterative loop lets the AI clean up its own mistakes before a human ever sees them.
How to do it
After the first draft, issue a follow-up prompt that asks the model to review its work against the criteria you provided and to produce an improved version.
Follow-up prompt
“Evaluate your draft against the success criteria stated earlier. Identify any violations, explain how you will fix them, and then provide a revised version.”
Pro tip
Set a limit on iterations to prevent infinite loops: “Repeat this critique-and-revise cycle a maximum of two times or until all criteria score 9 / 10 or higher.”
Putting It All Together: A Full-Body Prompt Example
Below is a single, composite prompt that embodies all seven rules. Feel free to adapt it to your own projects.
