“The limits of AI are defined not by its code, but by the questions we ask.”
Imagine you’re staring at an AI chatbot, typing what feels like a perfect question only to get a vague, unhelpful response. Frustrating, right? You’re not alone.
In the world of AI, the difference between mediocre and mind-blowing results often comes down to one skill: prompt engineering. The art of prompt engineering is asking not just what AI can do, but how it thinks. A well-structured prompt is half the answer.
Think of AI models as a mirror where what you get out depends on what you put in. They work on the principle of Garbage In, Garbage Out.
Why Prompting Matters
AI tools like ChatGPT, Deepseek and Claude are powerful, but they need clear instructions. Think of prompting as teaching a brilliant assistant how to help you. The better your guidance is, the better the results.
This isn’t just about asking questions. It’s about structuring requests so AI understands what you need and how you want it. That’s where prompt engineering frameworks come in.
5 Proven Frameworks for Better AI Responses
01. CRISPE Framework: The Master Chef’s Recipe
CRISPE (Clarity, Relevance, Iteration, Specificity, Parameters, Examples) ensures precise, well-structured prompts. It’s like having a master chef’s recipe that ensures consistent, delicious results every time.
- Capacity & Role (Define the AI’s role)
- Request (State the task clearly)
- Instructions (Provide step-by-step guidance)
- Style (Specify tone/format)
- Persona (Define voice/perspective)
- Experiment (Iterate for improvement)
This prompting framework works by removes ambiguity, keeping responses aligned with your goals, allows refinement through follow-ups and sets clear boundaries for AI output.
Best for: Strategic planning, content creation, and complex analysis.
Example:
Capacity & Role: Act as a business consultant with expertise in startups.
Request: Draft a one-page executive summary for a new fintech startup.
Instructions:
- Outline the company’s mission, target market, and unique value proposition.
- Include key financial projections for the first 3 years.
- Keep it concise but compelling for potential investors.
Style: Professional, persuasive, and data-driven.
Persona: Write as if you’re a seasoned entrepreneur pitching to venture capitalists.
Experiment: If the first draft is too technical, simplify it for a broader audience.
02. RTF Framework: The Reliable Three-Step Prompt
RTF (Role, Task, Format) simplifies AI communication into three parts.
- Role: Defines the AI’s role (e.g., “Act as a business consultant”)
- Task: specifies exactly what needs to be accomplished (e.g., “Analyze market trends”)
- Format: Sets the format (e.g., “Present findings in bullet points”)
Best for: Quick, structured requests like reports or summaries.
Example:
Role: Act as a career coach.Taks: Give me five actionable tips to improve productivity.
Format: Format as a numbered list.
03. TAG Framework: The Goal-Oriented Approach
The TAG framework (Task, Action, Goal) creates laser-focused prompts that drive toward specific outcomes. Think of it as your GPS for AI interactions which knows exactly where you want to go and the best route to get there.
Here is how and why it works:
- Task clearly defines what you want the AI to do
- Action details the specific approach or methodology
- Goal articulates the desired end result
Best for: Creative and analytical tasks where outcomes matter.
Example:
Task: Summarize a 1,000-word article on climate change.
Action:
- Break it into 3 key bullet points.
- Use simple language (grade 8 reading level).
- Include one surprising statistic.
Goal: Help a student quickly grasp the main ideas.
04. RODES Framework – The Detail-Oriented Consultant
If you want to have a consultant-level output from the ChatGPT or Gemini, RODES must be your go-to prompting framework. It is a structured method for crafting detailed, high-quality AI prompts. It stands for:
- Role: Who is the AI acting as? (e.g., “You’re a senior Python developer”)
- Objective: What’s the main goal? (e.g. “Fix a bug in a Flask API”)
- Details: Specific requirements/data (Key info, constraints)
- Examples: Provide examples for better output
- Style: Tone/format (Formal, casual, technical, etc.)
Best for: Copywriting, strategic planning, and research.
Example:
Role: SEO expert.
Objective: Improve blog traffic.
Details: Target keyword ‘AI prompt engineering.’
Example: [Insert a high-performing blog post].
Style: Ensure readability for beginners.”
05. RISEN Framework: The Step-by-Step Problem Solver
The RISEN Framework is a structured approach to crafting clear, actionable, and outcome-driven AI prompts. It breaks down complex tasks and provides a methodical approach to solving it. It worked by defining roles, specifying input data, outlining steps, setting expectations and narrowing the focus of AI model.
RISEN framework is the equivalent of a detailed project plan that leaves nothing to chance. It stands for:
- Role: Defines who is involved and their function.
- Input: Specifies the data or information provided.
- Steps: Outlines clear instructions for the AI to follow.
- Expectation: Sets clear goals and desired outcomes.
- Narrowing: Provides restrictions and focus areas.
Best for: Complex problem-solving (e.g., coding, research), creative projects (e.g., storytelling, ad campaigns), and precision tasks (e.g., legal docs, data analysis).
Example:
Role: You’re a children’s author like Roald Dahl.
Instructions:
- Write a 500-word story about a shy dragon who learns courage.
- Include humor, dialogue, and a moral (“Be yourself”).
- End with a twist (e.g., the dragon’s “flaw” saves the day).
Situation: For a 6–8-year-old audience; will be read aloud in class.
Examples: [Reference The Gruffalo’s rhythmic pacing].
Narrowing: Playful language, avoid scary scenes.
Pro Tips for Better Prompts
Drawing from the wisdom of experienced prompt engineers and AI professionals, here are the essential best practices that will elevate your prompting game:
- Be Specific and Detailed: Longer, more detailed prompts typically yield better results than short, vague requests. Think of specificity as your investment in quality. Tthe more context you provide, the better your returns.
- Use Natural Language and Delimiters: Write as if you’re talking to a knowledgeable colleague while using clear separators (like ### or —) to help the AI understand different sections of your request.
- Provide Context Like You’re Teaching: Imagine you’re explaining the situation to someone with no background knowledge. Include relevant details about your organization, audience, constraints, and objectives.
- Set Clear Goals and Expectations: The clearer your objective, the better the response. Specify not just what you want, but also the tone, style, and format you prefer.
- Give the AI Room to Think: Encourage step-by-step reasoning by adding phrases like “Let’s think through this step-by-step” to complex requests. This technique dramatically improves accuracy for analytical tasks.
- Use Scenario-Based Prompts: Create vivid scenarios that help the AI understand the context and stakes involved. This approach leads to more thoughtful, nuanced responses.
- Iterate and Refine: Expect some back-and-forth conversation to get optimal results. Think of prompting as a collaborative process rather than a one-shot command.
- Learn from other: There are communities over reddit where people share the prompts that work best for them, learn from them. You can also take help from tools like Prompt Genie or docsbot
Final Thoughts
There’s no single “best” framework, each has its strengths. The key here is the Experiment, adapt, and refine.
As AI becomes more integral to work, mastering prompt engineering will give you a huge advantage. Start small, keep practicing, and watch your AI interactions improve.
Your Turn:
Which framework works best for you? Have a favorite prompt hack? Share your experiences below and let’s learn from each other!