Too Many Frameworks, Not Enough Clarity
COSTAR. RISEN. APE. CRISPE. RACE. TIDD-EC. The prompt engineering world has produced dozens of frameworks, each with its own acronym and its own evangelists. If you’ve tried to figure out which one to learn, you’ve probably wasted more time reading about frameworks than actually writing prompts.
Here’s the truth: you do not need to learn them all. Most overlap significantly. Many are variations on the same core ideas. What you need is a clear understanding of 3–4 distinct approaches and a reliable way to decide which one fits your task.
This page gives you that. No fluff, no exhaustive taxonomy. Just a practical comparison of the four frameworks that cover nearly every real-world prompting scenario.
The Four Frameworks Worth Knowing
Out of the dozens of prompt frameworks available, four stand out as genuinely distinct approaches. Each solves a different problem, and together they handle virtually any AI task you will encounter.
COSTAR
RISEN
Chain-of-Thought
Few-Shot
Insight
Side-by-Side Comparison
This table gives you the full picture at a glance. Bookmark it and come back when you’re deciding which framework to use for a new task.
| Criteria | COSTAR | RISEN | Chain-of-Thought | Few-Shot |
|---|---|---|---|---|
| Full Name | Context, Objective, Style, Tone, Audience, Response | Role, Instructions, Steps, End Goal, Narrowing | Step-by-step reasoning before answering | Learning from provided input-output examples |
| Best For | Content creation, marketing copy, business writing | Multi-step workflows, SOPs, process documentation | Analysis, math, logic, debugging, decision-making | Classification, formatting, style matching, data extraction |
| Learning Curve | Low | Low-Medium | Very Low | Low |
| Unique Strength | Total control over output style and format | Clear sequencing and role assignment | Forces transparent reasoning, catches errors | Shows rather than tells, high consistency |
| Weakness | Can feel verbose for simple tasks | Less suited for creative or open-ended work | Adds length; not useful for simple generation | Requires good examples; garbage in, garbage out |
| Ideal User | Marketers, writers, business professionals | Operations managers, project leads, consultants | Analysts, developers, researchers | Data teams, QA engineers, content ops |
| Time to Write | 3-5 minutes | 3-5 minutes | 1-2 minutes | 2-10 minutes (depends on examples) |
| Works Best With | All models (GPT-4, Claude, Gemini) | All models; shines with instruction-tuned | Stronger models (GPT-4, Claude, Gemini Pro) | All models including smaller/faster ones |
COSTAR: When Structure Is Everything
COSTAR is the most comprehensive general-purpose framework. Its six elements — Context, Objective, Style, Tone, Audience, Response — give you fine-grained control over every dimension of the AI’s output.
It excels at content creation tasks where you care about both what the AI says and how it says it. Marketing emails, blog posts, business reports, product descriptions — any task where style and audience matter as much as substance.
Where COSTAR falls short: purely analytical tasks where style is irrelevant, or simple questions where the overhead is not worth it. You do not need six elements to ask “What is the capital of France?”
CONTEXT: I run a 15-person digital agency. We need to pitch a website redesign to a mid-size e-commerce client whose current site has a 70% bounce rate. OBJECTIVE: Write a one-page proposal summary that highlights the business case for a redesign, using their bounce rate and industry benchmarks. STYLE: Professional and data-driven. Short paragraphs, clear section headers. TONE: Confident and consultative. Not salesy. AUDIENCE: The client's VP of Marketing, who reports to the CEO and needs internal buy-in. RESPONSE FORMAT: - Executive summary (2-3 sentences) - Problem statement with data - Proposed approach (3 bullet points) - Expected outcomes with metrics
RISEN: When Process Matters
RISEN stands for Role, Instructions, Steps, End Goal, and Narrowing. It is built around the idea that AI performs best when given a clear identity and a sequential path to follow.
This framework shines for operational tasks: creating standard operating procedures, onboarding plans, project workflows, or any multi-step process where order is critical. The “Steps” element forces you to think through the sequence, and “Narrowing” keeps the AI from going off track.
RISEN is less effective for creative writing or open-ended brainstorming where you want the AI to explore freely rather than follow a defined path.
ROLE: You are a senior HR operations specialist with expertise in employee onboarding. INSTRUCTIONS: Create a 30-day onboarding plan for new software engineers joining a remote-first startup. STEPS: 1. Week 1: Equipment setup, account access, team introductions 2. Week 2: Codebase orientation, pair programming sessions 3. Week 3: First small ticket, code review process 4. Week 4: Independent work, 30-day check-in preparation END GOAL: The new hire should be able to independently pick up and complete standard tickets by day 30. NARROWING: Focus on engineering-specific onboarding. Do not cover general company orientation (HR handles that separately). Assume the team uses GitHub, Slack, and Linear.
Chain-of-Thought: When Reasoning Is Required
Chain-of-Thought (CoT) prompting is the simplest framework conceptually: ask the AI to think step-by-step before giving its final answer. That’s it. No acronym to memorize, no template to fill in.
But the results are striking. For tasks involving analysis, logic, math, or complex reasoning, CoT consistently outperforms direct prompting. The AI catches its own errors, considers edge cases, and arrives at more accurate conclusions.
CoT is not useful for simple generation tasks. If you need a marketing email, you do not need the AI to “reason through” it — you need COSTAR. Use CoT when the quality of the thinking determines the quality of the output.
Our SaaS product has three pricing tiers: Starter ($29/mo), Growth ($79/mo), and Enterprise ($199/mo). Last quarter, we had 200 Starter users, 80 Growth users, and 15 Enterprise users. This quarter, 30 Starter users upgraded to Growth, 10 Growth users upgraded to Enterprise, and we lost 25 Starter users entirely. Think through this step-by-step: 1. Calculate last quarter's MRR 2. Calculate this quarter's MRR after all changes 3. Break down the MRR change by component (new, expansion, contraction, churn) 4. Identify which tier transition had the biggest revenue impact 5. Recommend where to focus retention efforts and why
Few-Shot: When Examples Speak Louder
Few-shot prompting skips lengthy instructions in favor of examples. You show the AI 2–5 input-output pairs, then give it a new input and let it follow the pattern. The AI infers the rules from the examples rather than from explicit directions.
This approach is remarkably effective for classification, data extraction, format conversion, and style matching. It works especially well when the task is hard to describe in words but easy to demonstrate.
The downside: your examples must be high quality. If your examples contain inconsistencies or errors, the AI will replicate those too. And for tasks that require deep reasoning, examples alone are not enough — you need Chain-of-Thought.
Classify each customer support message as one of: billing, technical, feature-request, or general. Examples: Message: "I was charged twice this month for my subscription." Category: billing Message: "The export button gives me a 500 error when I click it." Category: technical Message: "It would be great if you added dark mode." Category: feature-request Message: "What are your office hours?" Category: general Now classify: Message: "My invoice shows the wrong plan name but the amount is correct." Category:
Decision Flowchart
Not sure which framework to use? Walk through these questions in order. The first “yes” gives you your answer.
“Do I need the AI to reason through a problem or show its work?”
Yes → Chain-of-Thought. Ask it to think step-by-step before answering.
“Do I need a specific output format, writing style, or classification?”
Yes → Few-Shot. Show 2–5 examples of the input-output pattern you want.
“Is this a multi-step process with a defined workflow?”
Yes → RISEN. Define the role, steps, and constraints explicitly.
“Do I need a comprehensive, well-structured response with control over style and tone?”
Yes → COSTAR. Fill in all six elements for maximum control.
Still not sure?
Start with COSTAR. It is the most versatile framework and works well for the widest range of tasks. You can always switch if you find a better fit.
Combining Frameworks
Here is the insight most framework guides miss: these approaches are not mutually exclusive. You can combine them when a single framework does not give you what you need.
The most common combinations:
- COSTAR + Chain-of-Thought: When you need structured output that also requires deep analysis. Use COSTAR for the format and CoT for the thinking.
- RISEN + Few-Shot: When you have a sequential process but want consistent formatting at each step. Define the process with RISEN, show examples with Few-Shot.
- Chain-of-Thought + Few-Shot: When you want the AI to reason through a problem but follow a specific reasoning pattern. Show examples of the reasoning steps you expect.
CONTEXT: I'm evaluating three project management tools (Asana, Linear, Monday) for a 30-person engineering team. Budget is $500/month. We need Jira migration support, GitHub integration, and sprint planning. OBJECTIVE: Analyze each tool against our requirements and recommend the best fit. STYLE: Analytical and structured. Use a comparison format. AUDIENCE: Engineering leadership team making the final purchasing decision. RESPONSE FORMAT: For each tool, provide: pricing fit, feature match (scored 1-5 per requirement), migration complexity, and overall recommendation. REASONING: Think through each tool's strengths and weaknesses step-by-step before scoring. Explain your reasoning for each score so we can validate your analysis.
Warning
Before & After
See the difference between an unstructured prompt and the same request using the right framework. This example uses COSTAR because the task — competitive analysis writing — needs control over structure, tone, and audience.
Write a competitive analysis of our product vs the top 3 competitors.
CONTEXT: We sell an AI-powered prompt builder (AskSmarter.ai) in a market with established competitors: PromptPerfect, FlowGPT, and PromptBase. Our differentiator is guided prompt construction through smart questions rather than manual template editing. We launched 6 months ago and have 2,000 active users. OBJECTIVE: Write a competitive analysis comparing our product against the three competitors across five dimensions: ease of use, output quality, pricing, target audience, and unique value proposition. STYLE: Direct and analytical. Use a comparison table for the five dimensions, followed by a narrative summary of our competitive position. TONE: Honest and balanced. Acknowledge competitor strengths. Do not spin weaknesses as advantages. AUDIENCE: Our founding team (CEO, CTO, Head of Product) preparing for a board meeting. They need facts, not cheerleading. RESPONSE FORMAT: 1. Comparison table (5 rows x 4 columns) 2. Key findings (3-4 bullet points) 3. Strategic recommendations (2-3 sentences) 4. Biggest competitive risk (1 paragraph)
Success
Quick Reference
Keep this table handy. When you start a new AI task, match it to the right framework.
| If Your Task Is… | Use This | Because… |
|---|---|---|
| Writing marketing copy or emails | COSTAR | You need control over style, tone, and audience |
| Creating an SOP or onboarding plan | RISEN | Sequential steps and defined end goals are critical |
| Analyzing data or making a decision | Chain-of-Thought | The reasoning process determines the quality of the answer |
| Classifying or extracting structured data | Few-Shot | Examples define the pattern better than instructions |
| Writing a business report or proposal | COSTAR | Multiple output dimensions (format, tone, audience) to control |
| Debugging code or finding logic errors | Chain-of-Thought | Step-by-step reasoning catches what direct answers miss |
| Matching a specific writing tone or format | Few-Shot | Showing the desired output is faster than describing it |
| Building a project plan or workflow | RISEN | Role assignment and step sequencing keep the output on track |
Next Steps
Now that you know which framework to use and when, dive deeper into the ones most relevant to your work:
- The COSTAR Method: A Complete Guide — The most versatile framework, explained element by element
- The RISEN Framework: A Complete Guide — Best for process-oriented and operational tasks
- Chain-of-Thought Prompting Guide — When reasoning quality is what matters most
- Few-Shot Prompting Guide — Learn by example, literally
Or skip the manual framework application entirely and let AskSmarter do it for you. Our prompt builder applies the right framework automatically based on your task.
Stop choosing frameworks. Start getting results.
AskSmarter asks you smart questions about your task, then applies the right framework (or combination of frameworks) automatically. You get optimized prompts without memorizing a single acronym.
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