Best Prompt Frameworks in 2026: CO-STAR vs RISEN vs RTF vs CRAFT (and When to Skip Them)

Short answer: Use RTF (Role, Task, Format) for quick everyday requests, CO-STAR when audience and tone matter, RISEN when the task has ordered steps and boundaries, and CRAFT as a general-purpose default. All of them are checklists for the same handful of ingredients: task, context, format, tone, and constraints. The best framework is the one that stops you from forgetting the ingredient you usually forget.

TL;DR

A prompt framework is a reusable template that prompts you to include what the AI would otherwise have to guess.
The five most common: RTF, CO-STAR, RISEN, CRAFT, and RACE.
Each one leaves something out. Only CO-STAR names the audience explicitly, and only RISEN names constraints explicitly.
Public head-to-head evidence is thin, and claims of "10x better results" deserve skepticism. Pick by fit, then test on your own tasks.
For throwaway prompts, skip the framework entirely.

What is a prompt framework?

A prompt framework is a structured template, usually an acronym, that breaks a request into labeled parts such as context, objective, tone, and format. Most people's default prompt contains one element: the task. Frameworks exist to fill in the rest: background, audience, constraints, and success criteria, the details that separate a useful output from a generic one.

Think of it like a recipe card. It doesn't make you a better cook, but it stops you from forgetting the salt.

The five frameworks at a glance

RTF: Role, Task, Format

The lightest option. You say who the AI should act as, what to do, and how to present it. It's the fastest to write and works best for simple, internal, low-stakes tasks like drafting an email or summarizing notes. Its weakness is that it says nothing about context, audience, or limits.

CO-STAR: Context, Objective, Style, Tone, Audience, Response

A six-part framework that originated with Singapore's government technology agency (GovTech). Its strength is audience awareness: separate Style, Tone, and Audience fields force you to think about who will read the output and how it should sound. That makes it a strong fit for marketing copy, customer communication, and anything brand-sensitive. The trade-off is length; it's overkill for a quick question.

RISEN: Role, Instructions, Steps, End Goal, Narrowing

Built for tasks with an obvious start, a clear finish, and conditional logic in between. The Steps field defines the sequence so the AI doesn't skip or reorder, and Narrowing sets boundaries so the output stays in scope. It has no dedicated place for background material or examples, so people tend to cram those into Instructions. It also doesn't specify output format unless you add it yourself.

CRAFT: Context, Role, Action, Format, Tone

A balanced five-part structure that covers most content-creation work. One 2026 guide recommends it as the default for roughly 80% of tasks, with RTF for quick one-offs and RISEN for multi-step jobs. It's heavier on setup than RTF but lighter than CO-STAR, and it lacks explicit fields for audience and constraints.

RACE: Role, Action, Context, Execute

A lean structure: role first, then the action, supporting context, then a directive to go. It's quick to write and focused on getting the task done, but it's less structured around format and tone.

Side-by-side: what each framework covers

Element	RTF	RACE	CRAFT	CO-STAR	RISEN	S.P.A.R.K.
Role / persona	✅	✅	✅	❌	✅	✅
Context / background	❌	✅	✅	✅	❌	✅
Task / objective	✅	✅	✅	✅	✅	✅
Output format	✅	❌	✅	✅	❌	❌
Tone / style	❌	❌	✅	✅	❌	✅
Named audience	❌	❌	❌	✅	❌	✅
Ordered steps	❌	❌	❌	❌	✅	❌
Explicit constraints	❌	❌	❌	❌	✅	✅
Iteration / testing loop	❌	❌	❌	❌	❌	✅

Mapped from each framework's published components; "❌" means there's no dedicated field, though you can always write it into another one.

The takeaway most comparison posts miss: no single framework covers everything. If you routinely forget your audience, use CO-STAR. If your outputs keep drifting out of scope, use RISEN. If you want speed, use RTF and accept the gaps.

Which framework should you use? A decision table

Your task	Best fit	Why
Quick email, summary, or rewrite	RTF	Fastest; low risk if it misses nuance
Marketing copy, brand voice, customer-facing text	CO-STAR	Audience, style, and tone are built in
Multi-step process (audit, plan, analysis with rules)	RISEN	Steps and narrowing keep it on track
Content creation across many topics	CRAFT	Balanced default
Fast task with some context	RACE	Lean and directive
Any task you'll reuse and improve	S.P.A.R.K.	Adds an explicit iteration step
Throwaway question	None	Structure overhead isn't worth it

Do prompt frameworks actually improve results?

Honestly: it depends, and the public evidence is thinner than the marketing suggests. New frameworks appear constantly with claims of dramatic improvements, and the few informal head-to-head tests published online use small samples and older models.

What is reasonable to say is that frameworks help because they force completeness. Vague prompts fail mainly because of missing information, not missing acronyms. A framework is a forcing function for including it.

That also explains a limit: on modern reasoning models, wording tricks matter less than supplying real context. A framework that prompts you for your situation is useful. A framework that just adds a persona line is mostly ritual. (For the wider picture, see our guide to what still works in prompt engineering.)

How to pick and test a framework in 10 minutes

Choose the task you do most often with AI.
Write it three ways: no framework, RTF, and the framework you think fits best.
Run all three on 3-5 realistic inputs.
Score outputs on criteria you set in advance: accuracy, tone, length, how much editing you had to do.
Standardize on the winner for that task type only.

Don't adopt one framework for everything. Different tasks need different ingredients.

Where the S.P.A.R.K. Method fits

S.P.A.R.K. is the framework Promhance applies automatically. Unlike the academic acronyms above, it folds an iteration step into the structure instead of treating testing as an afterthought:

S — Strategy & Search Intent: the outcome the prompt must achieve and the intent behind it, so the model knows what "done" looks like.
P — Persona & Purpose: who the AI should act as and who the result is for.
A — Authority & Attributes: the expertise, facts, style, and tone the answer should carry.
R — Refine & Restrain: tightening the wording and setting boundaries such as length, scope, and exclusions.
K — Keep Iterating: testing the output and improving it version by version rather than accepting the first draft.

Before (rough prompt):

Write a product launch email.

After (S.P.A.R.K.-structured):

You are a senior product marketer for a B2B SaaS tool. Write a launch email to existing trial users who have not upgraded, aiming to convert them before the trial ends. Use a confident, plain-spoken tone with no hype. Keep it under 180 words, lead with the single biggest benefit, include one specific proof point, and end with a clear one-line call to action. Then suggest two subject lines I can A/B test.

Where it sits: S.P.A.R.K. overlaps CO-STAR on audience and tone (Persona & Purpose, Authority & Attributes) and RISEN on boundaries (Refine & Restrain). The difference is Keep Iterating — the checklist frameworks assume you got it right the first time, while S.P.A.R.K. expects you to test and refine.

Frameworks only help if you actually use them, and most people don't stop to fill in six fields for a quick request. That's the idea behind Promhance, a free AI prompt enhancer built on the S.P.A.R.K. Method: paste a rough prompt, and it restructures it, adding the context, constraints, and format you left out, so you get the benefit of a framework without filling in a template by hand.

Try Promhance free →

Frequently Asked Questions

What is the best prompt framework in 2026?

There isn't one best framework. RTF is best for quick tasks, CO-STAR for audience- and tone-sensitive writing, RISEN for multi-step tasks with rules, and CRAFT is a solid general default.

What is the CO-STAR prompt framework?

CO-STAR stands for Context, Objective, Style, Tone, Audience, and Response. It originated with Singapore's GovTech team and is valued for making you define who the output is for and how it should sound.

What is the difference between RISEN and CO-STAR?

RISEN (Role, Instructions, Steps, End Goal, Narrowing) is built for sequential tasks with boundaries. CO-STAR is built for communication tasks where audience, style, and tone matter. RISEN lacks an audience field; CO-STAR lacks a steps field.

What does RTF stand for in prompting?

RTF stands for Role, Task, Format. It's the simplest common framework and suits quick, low-stakes requests.

Do I need a prompt framework to write good prompts?

No. A framework is a checklist, not a requirement. If you already include the goal, context, constraints, and format, you're doing the same job.

Do prompt frameworks work on ChatGPT, Claude, and Gemini?

Yes. They're model-agnostic because they structure your input, not the model. How much they help can vary by model and task, so test on your own use cases.

Can a tool apply a prompt framework for me?

Yes. Prompt enhancers like Promhance restructure a rough prompt automatically, which saves you from filling in a template every time.

Sources

Promplify, Prompt Engineering Frameworks Compared: CO-STAR, RISEN, RACE, CREATE, APE, and STOKE: https://promplify.ai/blog/prompt-engineering-frameworks-compared/
PromptQuorum, 8 Prompt Engineering Frameworks Explained: CRAFT vs CO-STAR vs APE (2026 Guide): https://www.promptquorum.com/blog/prompt-frameworks
sinc-LLM, Best Prompt Frameworks in 2026: RISEN vs CO-STAR vs sinc-LLM: https://sincllm.com/blog/best-prompt-frameworks
SurePrompts, The 10 Best AI Prompt Frameworks: Tested Templates for Better Results (2026): https://sureprompts.com/blog/ai-prompt-frameworks
MasterPrompting.net, Prompt Engineering Frameworks Compared: CRAFT vs RACE vs CLEAR vs CO-STAR: https://masterprompting.net/blog/prompt-engineering-frameworks-compared
Parloa, 12 Prompt Engineering Frameworks for Contact Centers: https://www.parloa.com/knowledge-hub/prompt-engineering-frameworks/