PROMPTS
8 ChatGPT Prompt Frameworks to Master AI ⚡
From R-T-F to R-I-S-E - eight proven frameworks that structure your prompts for dramatically better AI outputs.
The difference between a mediocre AI response and a brilliant one usually comes down to how you structure the prompt. These eight frameworks give you a repeatable system.
1. R-T-F (Role, Task, Format)
The simplest framework. Specify the role, task, and output format.
Act as a brand strategist. Write a messaging hierarchy for a B2B SaaS founder targeting finance teams. Use bullet points with core message, value props, proof, and CTAs.
2. S-O-L-V-E (Situation, Objective, Limitations, Vision, Execution)
Great for strategic planning. Define where you are, what you want, what constraints exist, where you want to end up, and how to get there.
Situation: At a startup that just launched a new B2B product.
Objective: Generate qualified inbound leads through educational content on LinkedIn.
Limitations: No ad spend. Small team. Must publish 3x/week.
Vision: Become the go-to thought leader for mid-market SaaS operations leaders.
Execution: Review performance biweekly and double down on top-performing content.
3. T-A-G (Task, Action, Goal)
Cut to the chase. Define the task, state the action, clarify the goal.
Task: Reduce customer churn in a SaaS subscription business.
Action: Analyse churn data and launch a targeted retention programme.
Goal: Improve customer retention rate by 15% over the next 6 months.
4. R-A-C-E (Role, Action, Context, Expectation)
Ideal for complex business prompts.
Role: Senior marketing strategist.
Action: Develop a content marketing plan.
Context: B2B SaaS company targeting mid-market operations teams.
Expectation: Tailored to buyer roles (Ops Lead, CTO, Procurement).
5. D-R-E-A-M (Define, Research, Execute, Analyse, Measure)
A full project lifecycle in one prompt.
Define: Declining retention in a B2B SaaS product.
Research: Through user interviews and CRM data.
Execute: Launch a 3-month retention programme with onboarding improvements.
Analyse: Compare behaviour between control and test cohorts.
Measure: Track churn rate, product usage frequency, and NPS.
6. P-A-C-T (Problem, Approach, Compromise, Test)
Perfect for decision-making with trade-offs.
7. C-A-R-E (Context, Action, Result, Example)
Provide context, describe needs, clarify results, give examples.
Context: Engagement with its onboarding flow.
Action: Redesign the onboarding journey to focus on quick wins and key features.
Result: Higher product adoption within the first 7 days.
Example: Show how the revised onboarding helped increase day-7 activation from 25% to 42%.
8. R-I-S-E (Role, Input, Steps, Expectation)
Great for data-driven tasks.
Role: Commercial director.
Input: Quarterly sales performance data and pipeline forecasts.
Steps: Identify weak points, prioritise high-conversion leads, and optimise resource allocation.
Expectation: Deliver a plan to exceed revenue targets in the next quarter.
Quick Decision Guide
The real unlock of AI agents is not full autonomy, but human-in-the-loop systems design. Structure your agent inputs with explicit constraints, negative prompts, and automated test checkpoints. When building tools, keep token consumption lean by caching system prompts and isolating tasks into specialized sub-agents.