For Simple Tasks
Start with Zero-Shot or Role Prompting. Clear instructions and a persona are often all you need.
A comprehensive reference to the most effective prompt engineering techniques and frameworks. Each framework is explained with practical examples, step-by-step instructions, and a detailed FAQ โ so you can choose the right approach for every AI interaction.
The most widely-used prompt engineering techniques โ learn these first.
Guide the model to reason through problems step by step, dramatically improving accuracy on complex tasks.
Learn more โExplore multiple distinct reasoning paths simultaneously and evaluate each branch before committing to a solution.
Learn more โGenerate multiple independent reasoning paths to the same question, then aggregate the most consistent answer.
Learn more โGive clear instructions without any examples, relying entirely on the model's pre-trained knowledge.
Learn more โProvide a handful of input-output examples so the model learns the exact pattern or format you need.
Learn more โAssign a specific role or persona to the model to unlock domain expertise and a consistent communication style.
Learn more โBreak complex tasks into sequential prompts where each output feeds into the next, building sophisticated pipelines.
Learn more โInterleave reasoning traces with concrete actions โ Thought โ Action โ Observation โ to solve tasks requiring tool use.
Learn more โRole, Task, Format โ the fastest role-based prompt template. Who you are, what to do, how to present it.
Learn more โRole, Action, Context, Expectation โ four components that cover who acts, what to do, the background, and the desired output.
Learn more โRole, Instructions, Steps, End Goal, Narrowing โ five parts covering who, what, how, outcome, and constraints.
Learn more โCapacity/Role, Insight, Statement, Personality, Experiment โ six components including tone and multiple-variation generation.
Learn more โContext, Objective, Style, Tone, Audience, Response โ six components popular for system prompt and content design.
Learn more โAttention, Interest, Desire, Action โ the classic 1898 direct-response formula adapted for AI-generated marketing copy.
Learn more โProblem, Agitate, Solution โ identify the pain, amplify it emotionally, then present the answer.
Learn more โDeeper and domain-specific frameworks โ reach for these when you need more precision or control.
Reason step by step but keep each step to a terse draft of a few words, cutting reasoning tokens by up to 80% versus Chain-of-Thought.
Learn more โAsk for the underlying principle or concept first, then use it to answer the specific question.
Learn more โDecompose a hard problem into simpler sub-problems and solve them in order, feeding each answer into the next.
Learn more โSystematically draft, test, evaluate, and revise prompts in iterative cycles until output quality meets your standard.
Learn more โDraft an answer, plan fact-check questions, answer them independently, then revise to remove hallucinations.
Learn more โLLMs generate verbal self-reflection on their own failures and use the linguistic feedback to improve subsequent attempts.
Learn more โMultiple AI instances debate opposing positions over several rounds; a judge synthesizes the most accurate final answer.
Learn more โApplies evolutionary algorithms โ crossover, mutation, selection โ to automatically optimize prompts across generations.
Learn more โCognitive-science-inspired prompting that structures inputs to activate specific reasoning modes and reduce cognitive load.
Learn more โThe simplest 3-part structure โ Task, Action, Goal โ for fast, goal-oriented prompts.
Learn more โAction, Purpose, Expectation โ a minimal 3-part framework that always asks why the task matters.
Learn more โRole, Input, Steps, Expectation โ a concise 4-part structure with an explicit Input component for data-driven tasks.
Learn more โContext, Action, Result, Example โ anchors every prompt with a concrete example to eliminate ambiguity.
Learn more โIntent, Details, Examples, Adjustments โ includes an Adjustments component for iterative, feedback-driven refinement.
Learn more โGoal, Reality, Options, Will โ the classic coaching model adapted for structured problem-solving prompts.
Learn more โPosition, Reason, Evidence, Position โ a concise argumentation formula for persuasive writing prompts.
Learn more โSituation, Task, Action, Result โ adapted from interview methodology for narrative and case-study prompts.
Learn more โInform, Explain, Example, Input โ a teaching-focused structure that builds context before processing your content.
Learn more โSpecific, Measurable, Achievable, Relevant, Time-bound โ classic project management criteria applied to prompt design.
Learn more โClarity, Relevance, Intent, Specificity, Precision โ a quality-evaluation checklist for diagnosing and improving any prompt.
Learn more โStructure prompts with Objective, Role, Context, and Action for clear, targeted AI responses.
Learn more โTask, Request, Action, Context, Example โ extends TAG with context and an example for richer, more precise prompts.
Learn more โBefore, After, Bridge โ contrasts the current problem with the desired state and shows how to get there.
Learn more โContext, Objective, Actions, Scenario, Task โ adds a Scenario component for situationally grounded, realistic prompts.
Learn more โRole, Aim, Input, Numeric Target โ forces quantifiable, measurable outputs with an explicit Numeric Target component.
Learn more โFunction, Level, Output, Win Metric โ performance-oriented framework that defines how success will be judged.
Learn more โProblem, Insights, Voice, Outcome โ problem-first framework that weaves brand voice into the prompt structure.
Learn more โSituation, End Goal, Examples, Deliverables โ plants full context for deliverable-focused project prompts.
Learn more โDirection, Existing Info, Expertise, Preferred Tone โ emphasizes tone specification for brand-sensitive content.
Learn more โContext, Logic, Expectations, Action, Restrictions โ adds Logic and Restrictions for precision and compliance tasks.
Learn more โQuestion, Understanding, Expectation, Scope, Time โ adds Scope and Time constraints for bounded research prompts.
Learn more โGoal, Understanding, Information, Direction, Evaluation โ defines success criteria explicitly for advisory tasks.
Learn more โFunction, Outcome, Criteria, Underlying Assumptions, Strategy โ makes hidden assumptions explicit for strategic prompts.
Learn more โRole, Action, Steps, Constraints, Examples โ comprehensive 5-part framework combining persona, process, guardrails, and examples.
Learn more โObjective, Scope, Constraints, Assumptions, Results โ project-management-inspired framework making scope and assumptions explicit.
Learn more โPurpose, Expectation, Context, Request, Audience โ audience-aware 5-part structure for targeted communication prompts.
Learn more โResearch, Hypothesis, Objectives, Development, Execution, Synthesis โ a rigorous 6-component academic research methodology.
Learn more โConceptualize, Research, Experiment, Analyze, Transform, Evaluate โ a six-stage creative process for structured innovation.
Learn more โSeven creative lenses โ Substitute, Combine, Adapt, Modify, Put to Another Use, Eliminate, Rearrange โ for systematic idea generation.
Learn more โStructures prompts to generate content optimized for search engine result pages and AI-generated search overviews.
Learn more โStart with Zero-Shot or Role Prompting. Clear instructions and a persona are often all you need.
Use Chain-of-Thought or Self-Consistency to force step-by-step reasoning and increase accuracy.
Use Few-Shot Prompting to show the model exactly what output structure you expect.
Use RISEN or CRISPE frameworks for structured, comprehensive prompts that leave nothing ambiguous.
Use Prompt Chaining to break large tasks into manageable, sequential steps with clear hand-offs.
Use ReAct or Tree of Thoughts when the model needs to reason, act, and adapt based on new information.
Which structured framework fits system prompts, task instructions or creative briefs.
Linear reasoning, branching search or majority vote: when each pays off.
When instructions are enough and when examples are worth the tokens.
Full reasoning or terse drafts: the accuracy, token and latency trade-off.
Why agents need the whole context window designed, not just the instruction.
Named multi-step algorithms that chain several of these frameworks in sequence for writing, coding, research and debugging.
A prompt framework is a reusable structure for writing prompts to large language models such as ChatGPT, Claude or Gemini. It names the parts a good prompt needs (for example Role, Task, Context and Format) or the reasoning procedure the model should follow (for example step-by-step Chain-of-Thought), so every prompt you write for that type of task is complete and consistent.
A prompt is a specific instruction given to an AI. A framework is a reusable, structural template that organizes how you write that instruction to guarantee consistent, high-quality outputs. A prompt solves one task once; a framework gives you a repeatable structure you can apply to any task of the same type.
Start with Zero-Shot prompting and add structure only when the output falls short. For most everyday tasks a short acronym framework such as RTF (Role, Task, Format) or RACE is enough. Use CO-STAR when tone and audience matter, Few-Shot when you need an exact output format, and Chain-of-Thought when the task involves reasoning, math or multi-step analysis.
Advanced reasoning frameworks like Tree of Thoughts and Multi-Agent Debate consume the most tokens because they require multiple API calls, branching logic, and self-evaluation passes. Self-Consistency also runs the same prompt several times to aggregate answers. Simple structural frameworks like RACE, RTF, and Zero-Shot have minimal token overhead.
Yes. You can easily combine structural frameworks with reasoning techniques. For example, use RISEN to structure your persona and task, while instructing the model to use Chain-of-Thought for its actual reasoning. See the Prompt Flows section for curated multi-framework sequences designed for specific real-world outcomes.
Most structural frameworks (like RACE or CRISPE) are model-agnostic and work reliably across current Claude, GPT and Gemini models. Reasoning models that think before answering already do much of what Chain-of-Thought asks for, so explicit step-by-step instructions matter less there. Advanced techniques like Tree of Thoughts and Multi-Agent Debate still need capable models: weaker models tend to collapse the branching logic or produce inconsistent self-evaluations.
Prompt Edit is a free macOS app that lets you save any of these prompt styles as reusable templates with dynamic variables. Write once, use everywhere โ completely offline and private.
Download Prompt Edit โ Free