The way we communicate with AI has shifted from simple trial-and-error prompts to something much more strategic: prompt design as a discipline. What began as short instructions typed into a chatbot is rapidly becoming an architectural craft - one that blends psychology, systems thinking, and creative engineering.
In my earlier blog on the types of prompts, I laid the groundwork by mapping out the different styles and approaches people use to interact with AI. That was the starting line. But if 2024 was about understanding the "grammar" of prompts, 2025 is about learning to design blueprints patterns that can be reused, adapted, and scaled across domains.
This is where the idea of the Prompt Architect comes in. Just as software architects don't just write code but design systems, prompt architects don't just write clever sentences they shape conversations into repeatable frameworks that unlock intelligence, creativity, and precision.
The Prompt Architect's Playbook: 12 Patterns Defining 2025 is my attempt to capture this evolution. These patterns are not tricks or shortcuts; they are design principles tested across industries and workflows that help transform prompting from an art of intuition into a craft of strategy. Think of it as moving from casual sketching to building with blueprints.
Wait… What's a Prompt Architect Anyway?
So, I know you may be wondering: "Prompt Architect? What kind of sci-fi job title is that?" Don't worry it's not about building skyscrapers for robots (yet). It's actually really simple. Think of it like this: there are certain blueprints you can use to ask Large Language Models (LLMs) questions in a way that makes them give you smarter, clearer, and more structured responses.
Instead of tossing random words at an AI and praying for magic, a Prompt Architect knows how to design the request so the model can unleash its full potential. It's like going from doodling stick figures to drafting a professional floor plan same pen, but wildly different results.
👉 In fact, prompting patterns are like recipes. You could throw ingredients randomly into a pot and hope it becomes dinner… or you could follow a blueprint (the recipe) and suddenly you're serving a gourmet dish.
So yes, a Prompt Architect is basically the AI world's version of a chef with a construction helmet designing recipes and blueprints that turn chaos into clarity.
Your Frameworks to Master in 2025: The Simple Guide
Think of AI like a super-smart, but literal-minded, new intern. If you just say "Help me with this project," they might get overwhelmed or do something completely wrong. Frameworks are like cheat sheets that tell your intern exactly how to think, what to do, and how to show you their work.
To make this easy, we'll use one real-world example for every single framework:
👉 You are a Product Manager. Your startup wants to add a personalized recommendation system (like Netflix or Spotify) to its mobile app. Your job is to use AI to help you plan, build, and launch this new feature.
Let's get started.
1. CO-STAR (Context → Objective → Style → Tone → Audience → Response)
What it is: A full, detailed recipe for your AI. It covers the situation, the goal, how it should sound, who it's for, and what the final answer should look like.
Why we use it: It gives the AI all the context it needs to give you a perfect, on-brand answer. It stops the AI from guessing and getting things wrong.
Detailed Example (Our Scenario):
- Context: "I am a product manager at a small startup. We are building a new feature for our app: a personalized recommendation system to suggest content to our users."
- Objective: "Create a one-page strategy document outlining the first step (MVP) for building this system."
- Style: "Write in clear, simple bullet points. Avoid complex technical jargon."
- Tone: "Be professional and confident, but also optimistic and encouraging."
- Audience: "This document is for our company's executives, who are not technical."
- Response: "Please provide the one-page strategy summary."
Analogy: It's like giving a chef a detailed recipe. You tell them what to cook (a chocolate cake), who it's for (a birthday party), the style (a layered cake), the tone (festive sprinkles), and how to present it (on a glass stand).
2. TCEF (Task → Context → Example → Format)
What it is: A simple, four-part formula that's easy to remember and gets great results fast.
Why we use it: It's quick and effective. The "Example" part is like showing the AI a picture of what you want, which is a powerful way to guide it.
Detailed Example:
- Task: "Brainstorm different types of recommendation algorithms we could use."
- Context: "Our startup is small and we only have data from about 10,000 users."
- Example: "For example, a 'collaborative filtering' algorithm suggests items based on what similar users like."
- Format: "Present the options in a table with three columns: 'Algorithm Name', 'Pros', and 'Cons'."
Analogy: Like filling out a order form online. You state your Task (I want to buy a shirt), give Context (size medium), show an Example (like this blue one), and choose the Format for delivery (ship it in a box).
3. CRISPE (Capacity → Role → Insight → Style → Persona → Example)
What it is: A super-powered version of CO-STAR. It doesn't just tell the AI what to do; it tells it who to be and how to think.
Why we use it: When you need the AI to act like a true expert—a seasoned CEO, a brilliant scientist, or a creative designer—and give you deep, insightful advice.
Detailed Example:
- Capacity & Role: "Act as a senior product strategist with experience in machine learning."
- Insight: "Focus on strategies that work for early-stage startups with limited data."
- Style: "Provide concise, actionable insights, not just theory."
- Persona: "You are a pragmatic PM who has built successful features at companies like Spotify."
- Example: "Structure your advice like a real product plan you would present to your team."
Analogy: It's not just hiring a chef; it's hiring Gordon Ramsay and telling him to use his knowledge of French cuisine (Insight), speak directly (Style), and cook you a dish just like he did on Hell's Kitchen (Example).
4. RTF (Role → Task → Format)
What it is: The simplest, fastest framework. Just three ingredients for a quick and useful output.
Why we use it: It's perfect for when you need a clear, structured answer in under 10 seconds. No fluff, just the facts.
Detailed Example:
- Role: "You are an expert UX designer."
- Task: "Draft the user onboarding flow for our new recommendation system."
- Format: "Provide the answer as a step-by-step user journey map."
Analogy: Like casting an actor for a play. You tell them their Role (you're the hero), their Task (save the kingdom), and how to Format their performance (do it as a dramatic monologue).
5. ICE (Instruction → Context → Example)
What it is: The "teacher's" framework. It's designed to help you learn or explain complex topics simply.
Why we use it: When you don't understand a concept and need the AI to break it down for you, or when you need to explain something complex to someone else.
Detailed Example:
- Instruction: "Explain what 'A/B testing' is."
- Context: "Explain it to me like I'm a high school student who has never heard of it before."
- Example: "For example, if we want to know if a blue button or a red button gets more clicks, we show the blue button to 50% of users and the red button to the other 50% and see which one performs better."
Analogy: Like a math teacher helping you with a problem. They give you the Instruction (solve for X), the Context (in this equation), and a similar Example problem they already solved on the board.
6. CRAFT (Capability → Role → Action → Format → Tone)
What it is: A precise and powerful framework for getting expert-level work. It focuses on the AI's capability and the specific action you want it to take.
Why we use it: For high-stakes tasks where every word and detail matters, like creating official documents, sophisticated strategies, or technical designs.
Detailed Example:
- Capability: "Use your knowledge of data analysis and product metrics."
- Role: "Act as a senior data scientist on my team."
- Action: "Identify the top 3 key performance indicators (KPIs) we should track to measure the success of our new recommendation system."
- Format: "Present this as a detailed table with the following columns: 'KPI Name', 'How to Measure It', 'Target Goal'."
- Tone: "Be highly professional and technical."
Analogy: Like a master carpenter CRAFTing a table. They use their Capability (woodworking skill), their Role (as a furniture maker), take Action (build a table), decide on the Format (a rustic oak dining table), and apply the final Tone (a smooth, polished finish).
7. APE (Action → Purpose → Expectation)
What it is: The laser-beam framework. It is incredibly focused and direct for when you know exactly what you want.
Why we use it: When you're in a hurry and need a very specific, no-nonsense answer without any extra explanation.
Detailed Example:
- Action: "List recommendation algorithms."
- Purpose: "To choose the best one for our MVP launch."
- Expectation: "Provide a ranked list of the top 3 most suitable algorithms, with a single sentence explaining why for each."
Analogy: Like a sniper. The Action is to pull the trigger, the Purpose is to hit the target, and the Expectation is a bullseye.
8. PECRA (Purpose → Expectation → Context → Request → Audience)
What it is: A framework built around the user. It forces you to think about who will read the AI's output and why.
Why we use it: Perfect for writing content that needs to persuade, inform, or appeal to a specific group of people, like a blog post, marketing email, or investor update.
Detailed Example:
- Purpose: "To persuade our executives to approve the budget for this project."
- Expectation: "A compelling one-paragraph argument."
- Context: "We need $50,000 and two months to build the MVP."
- Request: "Write the closing argument for my proposal."
- Audience: "The company's founders, who care most about user growth and retention."
Analogy: Planning a surprise party. The Purpose is to celebrate your friend, your Expectation is a fun party, the Context is their favorite pizza place, your Request is to book the back room, and the Audience is your friend and all their friends.
9. OSCAR (Objective → Scope → Constraints → Assumptions → Results)
What it is: The project manager's framework. It's designed for planning complex projects and making sure every detail is considered.
Why we use it: To create solid, realistic plans, timelines, and business cases. It helps you avoid missing important limits or making false assumptions.
Detailed Example:
- Objective: "Build and launch an MVP of our recommendation system."
- Scope: "This includes selecting an algorithm, building it into the app, and running a two-week test with 10% of our users."
- Constraints: "We have a budget of $25,000 and must launch within 3 months. Our team has only one developer."
- Assumptions: "We are assuming that users will engage with the recommendations at least once a day."
- Results: "The final result will be a report showing the click-through rate (CTR) of the new feature and its impact on user retention."
Analogy: Like planning a road trip. Your Objective is to get to the beach. The Scope is which states you'll drive through. Your Constraints are your budget and time. Your Assumption is that your car won't break down. The Result is you arriving and having a great time.
10. RASCE (Role → Action → Steps → Constraints → Examples)
What it is: A framework for creating clear, step-by-step instructions. It breaks down big, scary tasks into small, manageable pieces.
Why we use it: When you have a complex task and need the AI to create a checklist, a tutorial, or a standard operating procedure (SOP) for you or your team to follow.
Detailed Example:
- Role: "You are a project management consultant."
- Action: "Create a plan for building our recommendation system MVP."
- Steps: "Break the action down into 5 clear, sequential steps."
- Constraints: "The first step must take less than two weeks."
- Examples: "For each step, provide a real-world example of what the output should look like."
Analogy: Like a Lego instruction manual. It tells you the Role (you're the builder), the Action (build the spaceship), the Steps (do step 1, then 2, then 3...), the Constraints (use only these pieces), and shows you Examples (pictures of each step).
11. Reflection Pattern
What it is: You ask the AI to check its own work before giving you the final answer.
Why we use it: To dramatically improve the quality and accuracy of the AI's response. It catches mistakes, adds missing points, and improves clarity.
Detailed Example:
- First, you ask: "Draft a plan for our recommendation system MVP."
- Then you say: "Now, review the plan you just created. Act as a critical senior engineer. Identify any weaknesses, unrealistic timelines, or missing steps. Revise the plan to fix these issues."
Analogy: Like writing an essay and then using a spellchecker and grammar tool to reflect on your writing and fix all the errors before handing it in.
12. Flipped Interaction Pattern
What it is: You flip the script! Instead of you asking the AI questions, you command the AI to ask you questions first.
Why we use it: When you're stuck at the beginning of a project and aren't even sure what questions to ask. It helps you think more deeply and uncover requirements you hadn't considered.
Detailed Example:
- You say: "I need to build a recommendation system. To give me the best advice, please ask me 10 critical questions you need the answers to first."
- The AI will ask: "1. How many users do you have? 2. What type of content are you recommending? 3. What is your technical team's expertise? 4. What is your budget?" etc.
- You answer them.
- Then the AI can give you a perfect, customized plan.
Analogy: Like going to a doctor. You don't just say "I'm sick, fix me." A good doctor flips the interaction and first asks you: "Where does it hurt? How long has it been going on? What are your symptoms?" After you answer, they can give an accurate diagnosis.
🚀 Final Takeaway: Your Blueprints for Success
These 12 frameworks are not the finish line they're just a milestone. Think of them as the "starter pack." Out there, you'll find 50+ other frameworks floating around the internet, each with its quirks. I only shared the most popular and useful ones that I've personally tested, tinkered with, and occasionally yelled at when they didn't cooperate.
Start small. Play with the lighter ones like TCEF or RTF they're like training wheels. As you get more comfortable, move up to CRAFT and OSCAR for bigger, more complex projects.
But here's the fun part: you don't have to stick to what's already out there. If you discover a structure or blueprint that consistently gets you better results, congratulations you've just invented your own framework! (Yes, you're now a Prompt Architect yourself. Fancy title unlocked.)
At the end of the day, the goal is simple: stop treating AI like a magic 8-ball you shake with vague questions, and start treating it like the world's most powerful intern one that thrives when you give it clarity, detail, and purpose.
So experiment, remix, invent. And hey, if you've got a killer framework trick up your sleeve, drop it in the comments I'd love to see how you're bending AI to your will.
🔗 Connect with Me
📖 Blog by Naresh B. A.
👨💻 Aspiring Full Stack Developer | Passionate about Machine Learning and AI Innovation
🌐 Portfolio: [Naresh B A]
📫 Let's connect on [LinkedIn] | GitHub: [Naresh B A]
💡 Thanks for reading! If you found this helpful, drop a like or share a comment feedback keeps the learning alive.❤️
Top comments (0)