Prompting Best Practices

Prompting Basics

Understand the Process

Understanding how Large Language Models (LLMs) work is crucial for crafting prompts that lead to the most usable and relevant outcomes. When you're unsure about what's happening under the hood with ChatGPT, Gemini, and Claude, using them can feel like navigating a complex system blindly, which often leads to frustration and subpar outputs. A quick review of our How Does an LLM Work page makes the general concept easy to grasp and will help you get more out of the information below.

Strive for Clarity Balanced with Details

Your mission is to find the sweet spot between details and clarity. When prompts are vague, ambiguous, or do not provide enough details, the AI will make assumptions. Long, complicated, and layered descriptions, requests, and examples in a prompt can confuse the LLM.

Keep it simple and have a conversation with the AI. It’d be counterintuitive to dominate a conversation with a human by overloading them with repetitive details and multiple explanations. Longer, detail-heavy prompts will rarely get you better results. Clarity beats complexity.

Likewise, even a slight lack of clarity could cause confusion. For example, when you want to know what day of the week someone was born on, and ask, “What day were you born?” The probability of them responding with a month, day, and year is nearly 100%. So, you clarify and ask again. Same thing here.

Remember, the LLM’s response to your prompt is determined by you. The often amazing, mind-blowing response you receive is a direct result of what you created. You utilized a creative tool to achieve a specific result.

Prompt Frameworks

You’ll find these frameworks to be helpful for specific outcomes, but keep in mind that they should be used when you have a clear objective, such as a script, a report, a list, or instructions delivered in a specific format, with multiple versions, etc.

The majority of your experiences with prompting any LLM will be conversational. When you're looking for information or want to generate ideas, explore new concepts, create lists, or similar tasks, a free-flowing conversation with the LLM is best.

The Best General Use Framework For ChatGPT, Claude, and Gemini

1. Role: Instruct the AI to"act as" or "take on the role of" a persona, or character, to influence the style, tone, and content of its response

2. Context: What’s the task? Who’s it for? What conditions should be adhered to?

3. Goal: This is the ‘why.’ A clear description of your goal deepens clarity, which in turn refines the response.

4. Output Rules: Specify tone, format, word count, or any boundaries.

Example:
Role: You are an expert social media strategist with a great sense of humor.
Context: Outline a four-week creative social content plan that increases tune-in for our station’s weekday afternoon“Drive at Five.”
Goal: Make our audience aware that we hand-pick the perfect songs for their drive home, as that may be the only thing that motivates them to go back to work tomorrow.
Output Rules: The outline should include ideas for posts with a message that evolves in a natural and entertaining way over a four-week. Include the best times to post on different social platforms.

The Mack Truck of Frameworks
Basic, reliable, easy to customize, good for generalized structured responses.

1. Context: What’s the task? Who’s it for? What should it achieve?

2. Guardrails: Specify tone, format, word count, or any boundaries.

3. Reference: Include an example or explain what good looks like.

Example:
Context: Make two lists of things parents think about; 1 - on their child’s first day of kindergarten, and 2 - on their child’s first day of their senior year in high school.
Objective: Make it relatable with sharp humor, do not use profanity.
Style/Tone: Format the lists side by side, like:
Kindergarten: Senior Year:
I hope they’ll be OK I hope they get a job

CO-STAR
(Context → Objective → Style → Tone → Audience → Response)

Best for general tasks that require clear, well-defined outputs.

  • Context: The background facts, inputs, and constraints the model must consider

  • Objective: The single outcome you want (what success looks like).

  • Style: Writing characteristics (e.g., concise, story-driven, technical).

  • Tone: Emotional vibe (e.g., friendly, authoritative, playful).

  • Audience: Who it’s for and their knowledge level.

  • Response format: The exact structure/length/filetype you want back.

Example:
Context: You’re writing a promo for a country radio station.
Objective: Write a 30-second script teasing a new contest announcement.
Style/Tone: Spark curiosity/high energy, fun.
Audience: Fans of our station and country music.
Response: 3 alt scripts, each with a one-line hook to open, and a close driving Friday 7 AM tune-in.

RTCCO
(Role → Task → Context → Constraints → Output)

Best for achieving precise and predictable deliverables.

  • Role: The hat the model wears (DJ, Social Media writer, Producer)

  • Task: The concrete action to perform (write, compare, outline).

  • Context: Relevant details, inputs, and situational info.

  • Constraints: Non-negotiable rules (length, do/don’t, citations, sources).

  • Output: Required shape of the result (bullets, table, script, article, etc.)

Example:
Role: Marketing copywriter.
Task: Draft a 30 video script.
Context: Find out how to win $500 five times a day from (station), listen on Friday at 7 AM.
Constraints: Make script VO fill 20 seconds total, include two 5-second listener drops, upbeat tone in
plain English, no buzzwords, no cheesy radio-sounding language, suggest visuals.
Output: 3 script versions, attention-grabbing opener, 3 CTA options throughout all scripts.

AIDA
(Attention → Interest → Desire → Action)

Best for promos, spots, social posts, landing page, app notifications.

  • Attention: Hook

  • Interest: Why it matters, The emotion it sends

  • Desire: Benefits/proof

  • Action: CTA

Example:
Attention: Write a Facebook post for a $500 contest
Interest: Free money for the taking, splurge!
Desire: $500 goes a long way if you know how to have fun.
Action: Learn how to win, Friday 7AM

PAS
(Problem → Agitate → Solve)

Best for short, persuasive copy and intros.

  • Problem: The core pain or friction.

  • Agitate: Make the impact feel real (stakes, consequences).

  • Solve: Present the solution and how to start.

Example:
Problem: You missed (morning show bit) today
Agitate: Very sad for you, it was the best one we’ve ever done.
Solve: Lucky you! We posted the entire bit on our website, mobile app, and Facebook.

RATT
(Role → Audience → Topic → Task)

Best for quick, lightweight prompts when you’re in a hurry.

  • Role: The perspective/expertise to adopt.

  • Audience: Who will consume it and what they know.

  • Topic: The subject or theme to cover.

  • Task: The action/output required on that topic.

Example:
Role: PD/Ops Mgr.
Audience: New on-air staff.
Topic: Localization tips.
Task: Write a 200-word cheat sheet with 5 do’s and 3 don’ts.

Prompt Templates

Copy, Customize, Deploy

How to Help the LLM Help You

Prompting is far from a one-size-fits-all experience. You still need to stay in a ‘learn and adapt’ mindset, but generally, prompting continues to get easier thanks to the improved reasoning capabilities of the newest large language models (LLMs) used by ChatGPT, Claude, Gemini, and others.

Contrary to what you may have heard, there is no single “best” way to write a prompt. We’ve included prompt frameworks above because they can be a significant help and have been proven to work. We’ve learned some AI users may find them somewhat cumbersome. Trying to accurately craft prompt structure “ingredients” can slow the process down for some people, which is the exact opposite of what you should expect when using AI.

To that end …

The more you understand how the AI platform, tool, or LLM you're using was taught to absorb and interpret what you’re asking it to do, the better you become at prompting. Try these techniques a few times before relying solely on a prompt structure:

Prompt AI to improve your prompt

This is the single most effective way to learn how to get more useful responses and actions. Enter a prompt like this: “I’m trying to accomplish (X). This is the prompt I’ve created.” (insert the prompt you intend to use) “How would you improve this prompt to get a better answer from you?”

This technique makes the LLM reveal what it knows to you, so you can apply that understanding in the future. You’re basically asking it to show you what it needs in order to provide responses you can use. You’re using a prompt in your prompt to get a prompting lesson.

Ask for a clarity check

“This is the prompt I’ve created.” (insert the prompt you intend to use). “What parts of this request are unclear, vague, or ambiguous? What assumptions are you making? Is there additional information that would help you improve the accuracy of your response?”

By asking these questions, you’re making the LLM reveal its uncertainties that it would otherwise infer or assume, so it can proceed. But now you’ve got it talking about the info, data, examples, context, and other things you may not have realized it needed. Asking for a clarity check allows you to fill in the blanks you didn’t know existed, rather than turning those decisions over to the LLM (which can be the source of AI hallucinations).

Capability discovery

This is a great technique to use when you've prompted a few times and feel like you're banging your head on the wall because the LLM’s responses aren’t what you're looking for. The prompt: “How would you approach this if you had no constraints? What would be your ideal process? What tools or information would help?”

This technique can sometimes reveal prompting approaches you hadn’t thought of and information you can provide to help improve its output. Asking more questions about the tools and information the LLM mentions, like, “Tell me how you’d use that information,” or, “Why is that tool helpful?” can help the LLM draw a clearer picture of how you can help it help you.

Request a step-by-step plan before asking for execution


This allows the LLM to lay out its thinking for you to critique and adjust before it begins working on your request. This can make a big difference in the outcome of a layered or detail-heavy prompt. This concept is similar to the technique known as Chain Prompting: asking the LLM to ‘think step by step’ in a single prompt that includes many planning and execution requests. Chain prompting is quite effective, but we also recommend breaking chain prompt requests into individual prompts, allowing you to review the responses one by one, and, if needed, modify them or ask questions before moving to the next step of your request.

Chain Prompt Example:

"Write a research paper on the impact of social media on mental health. First, outline the key sections of the research paper (introduction, literature review, methodology, results, discussion, conclusion). Second, suggest a logical flow for each section. Third, suggest potential research questions and hypotheses. Finally, create an outline with key points for each section." 

Plan-Solve Example:

Prompt #1 - Help me create a plan for a research paper on the impact of social media on mental health. First, outline the key sections of the research paper (introduction, literature review, methodology, results, discussion, conclusion).

Review the response, ask for changes or explanations. Move to Prompt #2 when approved.

Prompt #2 - Suggest a logical flow for each section.

Review

Prompt #3 - Suggest potential research questions and hypotheses.

Review

Prompt #4 - Create an outline with key points for each section.

Considering the nature of these four prompts, one can see how the outline being created in Prompt #4 will be clearly defined and on point with the user’s expectations, as they were able to guide the LLM’s thinking and decisions on the key sections, logical flow, and research.

Pro Tips

Stop typing. Start talking.

Our brains move faster than we can type. Because ChatGPT, Claude, Gemini, and others are built to work with plain language prompts, simply clicking the microphone and talking to them is the fastest and easiest way to get what you're looking for and pick up new skills

If you’re uncomfortable recording your voice directly into any AI platform, use the speech-to-text function on your computer or phone. Five years ago, it seemed odd to see someone looking at their phone while they talked to it to write a text message. It's become the norm today and should be the norm when you're prompting AI.

Choose Your AI Wisely

Using the same prompt in ChatGPT and Claude will always yield different results. The same is true for Gemini, Copilot, Perplexity, and any other AI Assistants, Chatbots, LLMs, etc. That’s why it’s important to understand their individual strengths and weaknesses.

Armed with this knowledge, you’ll have the confidence to use multiple platforms and LLMs. The vast majority of people who say they’ve used AI mean, “I’ve used ChatGPT.” It may not be true today, but in the coming months, using only one LLM will put you at a disadvantage.

See our AI Chatbot/LLM comparison graphic and capabilities & features resource to get up to speed on the basics.