Do you ever feel like talking to AI should be easier? You are not alone!
As powerful AI models become part of everything from writing assistants to customer service bots, learning how to “talk” to them effectively has become an art and science.
That’s where the prompting frameworks come in. Think of them as the cheat codes for getting great results from large language models (LLMs). Whether sticking to tried-and-true methods or exploring advanced experiments, the right framework helps you craft better prompts, get more accurate answers, and save time.
In this blog, we will walk you through well-established and experimental prompting frameworks—how they work, when to use them, and what makes each unique.
Standardized Prompting Frameworks
Let’s look at the foundational frameworks and structured approaches that have been widely adopted for their reliability and consistency:
1. Chain-of-Thought (CoT) Prompting
Chain-of-Thought prompting, introduced by Wei et al. (2022), encourages LLMs to break down complex reasoning tasks into sequential steps.
Methodology: Instruct the model to “think step by step” or provide examples of step-by-step reasoning before asking the target question.
Example:
Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
A: Let's think step by step.
1. Roger starts with 5 tennis balls.
2. He buys 2 cans of tennis balls.
3. Each can has 3 tennis balls.
4. So the 2 cans have 2 × 3 = 6 tennis balls.
5. The total number of tennis balls is 5 + 6 = 11.
Strengths:
- Dramatically improves performance on reasoning and math problems
- Reduces logical errors by making intermediate reasoning steps explicit
- Makes model reasoning more transparent and auditable
Limitations:
- Can increase token usage significantly
- May not help with tasks that don’t benefit from explicit reasoning steps
Ideal Use Cases: Mathematical reasoning, logical puzzles, multi-step problem solving
2. Few-Shot Prompting
Few-shot prompting provides the model with examples of the desired input-output pattern before the actual query.
Methodology: Present multiple task examples with correct responses before introducing the query.
Example:
Input: The movie was entertaining and enjoyable.
Output: Positive
Input: The service was slow and the food was cold.
Output: Negative
Input: The lecture was informative but somewhat boring.
Output:
Strengths:
- Reduces ambiguity by clearly demonstrating the expected format
- Helps models understand unusual or specialized tasks
- Requires no model fine-tuning
Limitations:
- Consumes significant prompt space with examples
- Examples may inadvertently bias model outputs
- Performance plateaus after a few examples
Ideal Use Cases: Classification tasks, format conversion, domain-specific tasks
3. REACT (Reasoning and Acting)
REACT, introduced by researchers at Princeton and Google, combines reasoning and action in an iterative process.
Methodology: Following a cycle of Thought, Action, Observation:
- Thought: The model reasons about the current state and what to do next
- Action: The model takes an action based on its reasoning
- Observation: The model observes the result of its action
Example:
Question: What is the capital of the country with the highest population?
Thought: I need to determine which country has the highest population and then find its capital.
Action: Find the country with the highest population
Observation: China has the highest population with about 1.4 billion people.
Thought: Now I need to find the capital of China.
Action: Find the capital of China
Observation: The capital of China is Beijing.
Answer: Beijing
Strengths:
- It combines reasoning with action planning
- Enables complex problem solving through iterative refinement
- Creates transparent decision trees
Limitations:
- Verbose, consuming significant token space
- It may be overkill for simpler tasks
Ideal Use Cases: Complex multi-step tasks, programming problems, information gathering tasks
4. Role Prompting
Role prompting assigns a specific identity or expertise to the model to elicit responses from a particular perspective.
Methodology: Instructing the model to assume a specific role, profession, or perspective before responding to the query.
Example:
You are an experienced pediatrician with 20 years of clinical practice.
What advice would you give to parents concerned about their child's fever?
Strengths:
- Helps focus model responses toward specific expertise areas
- Can improve the depth and relevance of specialized knowledge
- Helpful in obtaining different perspectives on the same issue
Strengths:
- May sometimes lead to overconfidence in specialized domains
- Role limitations need to be clearly defined to prevent overreach
- Can potentially reinforce stereotypes if not carefully designed
Ideal Use Cases: Specialized advice, creative writing, perspective analysis
5. CRISP (Cognition Representation Instruction Style Purpose)
The CRISP framework, developed by David Shapiro, structures prompts along five dimensions for comprehensive control.
Methodology: Addressing five key elements:
- Cognition: The thinking style (analytical, creative, etc.)
- Representation: Output format and structure
- Instruction: Specific task directives
- Style: Tone, voice, and linguistic characteristics
- Purpose: The Ultimate goal of the interaction
Example:
Cognition: Think critically and methodically
Representation: Create a structured table with pros and cons
Instruction: Analyze electric vs. gas vehicles
Style: Use neutral, factual language with technical terms when appropriate
Purpose: To help a consumer make an informed purchase decision
Strengths:
- Comprehensive control over multiple dimensions
- Produces consistent, well-structured outputs
- Adaptable to various task types
Limitations:
- Requires more elaborate prompt construction
- It may be unnecessarily complex for simple tasks
Ideal Use Cases: Complex analysis tasks, formal business communications, detailed reports
6. Persona Prompting
Persona prompting extends role prompting by creating detailed character profiles to guide model responses.
Methodology: Providing the model with a detailed persona description, including background, expertise, values, and communication style.
Example:
Persona: You are Dr. Maya Chen, a 45-year-old climate scientist with a Ph.D. from MIT who specializes in oceanic carbon capture technologies. You have 20 years of field experience across three continents and have published over 70 peer-reviewed papers. You communicate with technical precision but can explain complex concepts using analogies. You prioritize evidence-based conclusions and remain hopeful yet realistic about climate solutions.
Question: How effective are current direct air capture technologies in addressing climate change?
Strengths:
- Enables consistent character voice across interactions
- It combines expertise with communication style guidance
- Particularly effective for creative and storytelling applications
Limitations:
- Detailed personas consume significant token space
- May lead to unnecessarily stylized responses for factual queries
Ideal Use Cases: Creative writing, roleplaying scenarios, storytelling, consistent character development
These standardized prompting methods set the stage for effective, repeatable interactions with large language models
Experimental Prompting Frameworks
Here are some experimental prompting frameworks that explore novel, adaptive techniques that challenge traditional norms:
1. Midjourney Prompting
Emerging from the Midjourney image generation community, this style uses detailed, layered descriptor sets.
Methodology: Combining multiple aesthetic, technical, and stylistic descriptors with ratio weights.
Example:
Analyze this business scenario as if you were:
{McKinsey strategy consultant::3} {Harvard economics professor::2} {Serial entrepreneur::1}
Using frameworks: {Porter's Five Forces::2} {SWOT Analysis::1} {Blue Ocean Strategy::1}
In the style of: {data-driven::3} {practical::2} {innovative::1}
Strengths:
- Provides fine-grained control over multiple influences
- Allows weighting of different aspects
- Highly customizable for creative applications
Limitations:
- Experimental with varying effectiveness across models
- Complex syntax can be challenging to master
- May produce inconsistent results
Ideal Use Cases: Creative analysis, mixed-perspective evaluations, experimental outputs
2. Tree of Thoughts (ToT)
Tree of Thoughts extends Chain-of-Thought by exploring multiple reasoning paths simultaneously.
Methodology: Generating and evaluating several possible thought chains at each step to determine the most promising direction.
Example:
Problem: What's the next number in the sequence 3, 7, 15, 31, ...?
Path 1:
- The difference between consecutive terms: 4, 8, 16. Each difference is doubling.
- If this pattern continues, the next difference would be 32.
- So the next number would be 31 + 32 = 63.
Path 2:
- Let's check if there's a formula: 2^n + 1, for n starting at 1.
- 2^1 + 1 = 3, 2^2 + 1 = 5 (doesn't match 7), so this isn't right.
Path 3:
- Let's try 2^n - 1, for n starting at 2.
- 2^2 - 1 = 3, 2^3 - 1 = 7, 2^4 - 1 = 15, 2^5 - 1 = 31.
- If this pattern continues, the next number would be 2^6 - 1 = 63.
Both Path 1 and Path 3 arrived at 63, so that's likely the answer.
Strengths:
- Explores multiple solution strategies simultaneously
- Can handle problems with uncertain or ambiguous approaches
- More robust than single-path reasoning
Limitations:
- Extremely token-intensive
- Complex to implement effectively
- It can be overwhelming for simple problems
Ideal Use Cases: Complex reasoning problems, situations with multiple viable strategies, creative problem-solving
3. Skeleton of Thought (SoT)
Skeleton of Thought first outlines the high-level structure of the response before filling in details.
Methodology: Creating a skeletal outline of the reasoning process or response structure, then systematically expanding each section.
Example:
Question: Write a comprehensive analysis of renewable energy adoption challenges.
Skeleton:
1. Introduction to renewable energy landscape
2. Technical challenges
2.1. Intermittency issues
2.2. Grid integration
2.3. Storage limitations
3. Economic challenges
3.1. Initial investment costs
3.2. Subsidies and market competition
4. Policy and regulatory challenges
5. Social and cultural barriers
6. Recommended strategies to overcome challenges
7. Conclusion
Now I'll expand on section 1:
[detailed expansion of section 1]
Now I'll expand on section 2.1:
[detailed expansion of section 2.1]
...and so on
Strengths:
- Ensures comprehensive coverage of complex topics
- Maintains logical structure throughout lengthy responses
- Helps avoid omissions in complex analyses
Limitations:
- Can be unnecessarily structured for simple queries
- May lead to overly formulaic responses
- Consumes additional tokens for outlining
Ideal Use Cases: Complex analyses, comprehensive reports, structured documents
4. Automatic Reasoning and Tool-use (ART)
ART prompting empowers models to automatically select and use tools when needed.
Methodology: Instructing the model to recognize when external tools (calculators, search, code execution) would be beneficial and to request them explicitly.
Example:
When answering the following question, if you need to perform a calculation, write [CALC] followed by the expression, and if you need to run code, write [CODE] followed by the code to execute.
Question: What is the compound interest on $10,000 invested for 5 years at 7% annual interest, compounded monthly?
[CALC] 10000 * (1 + 0.07/12)^(12*5)
Result: $14,191.02
Therefore, the compound interest would be $14,191.02 - $10,000 = $4,191.02.
Strengths:
- Improves accuracy on computational tasks
- Creates clear interfaces for tool integration
- Makes the reasoning process explicit and verifiable
Limitations:
- Requires an interpretation layer to execute tools
- Can be inefficient if overused
- Not standardized across systems
Ideal Use Cases: Mathematical problems, programming tasks, scenarios requiring external data
5. Active-Prompt
Methodology: The model generates its response, evaluates it against explicit criteria, and then refines it until it meets quality thresholds.
Example:
User-agent: GPTBot
Allow: /
User-agent: Google-Question: Explain the greenhouse effect in simple terms.
Initial Response: The greenhouse effect is when gases in the atmosphere trap heat from the sun.
Self-evaluation:
- Accuracy: 7/10 (oversimplified, missing key components)
- Completeness: 4/10 (doesn't explain mechanism or gases involved)
- Clarity: 8/10 (very simple, but perhaps too simple)
Improved Response: The greenhouse effect is like a blanket around Earth. Sunlight passes through the atmosphere and warms the Earth's surface. This warmth then radiates back toward space as heat. Greenhouse gases like carbon dioxide and methane trap some of this heat, similar to how glass in a greenhouse keeps plants warm. This natural process keeps Earth at a comfortable temperature, but too many greenhouse gases can cause excessive warming.
Self-evaluation:
- Accuracy: 9/10 (includes mechanism and key concepts)
- Completeness: 8/10 (covers main points with appropriate detail)
- Clarity: 9/10 (uses accessible analogy while maintaining accuracy)
Final Response: [Improved Response]Extended
Allow: /
Strengths:
- Creates self-improving outputs
- Makes evaluation criteria explicit
- Can improve response quality without user iteration
Limitations:
- Extremely token-intensive
- May lead to overly verbose responses
- The criteria need careful specification
Ideal Use Cases: Critical content generation, educational materials, sensitive communications
6. Multimodal Prompting
Multimodal prompting combines text instructions with other media types like images, audio, or video.
Methodology: Providing the model with a combination of text prompts and other media inputs, with specific instructions on interpreting and using the non-text elements.
Example:
[IMAGE: Chart showing quarterly sales data]
Analyze the trends visible in this sales chart. Identify key growth periods and potential concerns. Then recommend three data-driven strategies to address any negative trends.
Strengths:
- Enables analysis of non-text information
- Combines multiple information sources
- Often more efficient than describing visual elements textually
Limitations:
- Requires multimodal model capabilities
- Interpretation quality varies across models
- Limited standardization
Ideal Use Cases: Image analysis, data visualization interpretation, and technical documentation analysis
These approaches unlock new capabilities and enhance model performance in dynamic contexts.
Comparative Analysis and Selection Criteria
Choosing the right prompting framework requires carefully comparing effectiveness, flexibility, and context-specific performance.
When choosing a prompting framework, you must consider:
- Task Complexity: Simple tasks may need only basic prompting, while complex reasoning benefits from CoT or ToT approaches
- Token Efficiency: Consider budget constraints and response time requirements
- Transparency Requirements: Some frameworks offer more visible reasoning processes
- Domain Specificity: Certain frameworks excel in specific domains (e.g., math, creative writing)
- Model Capabilities: Different models respond better to different prompting techniques
Let’s summarize the key selection criteria to help you make informed, strategic decisions:
| Framework | Token Efficiency | Reasoning Transparency | Implementation Complexity | Best For |
|---|---|---|---|---|
| Few-Shot | Medium | Low | Low | Classification, format tasks |
| Chain-of-Thought | Low | High | Medium | Math, logic problems |
| REACT | Very Low | Very High | High | Multi-step problems |
| Role/Persona | Medium | Medium | Medium | Specialized knowledge, creative |
| CRISP | Medium | Medium | Medium | Structured analysis |
| Tree of Thoughts | Very Low | Very High | Very High | Complex, uncertain problems |
The Future of Prompting Frameworks
As AI capabilities grow, prompting frameworks evolve in parallel and shift from static templates to adaptive, context-aware systems.
Here are some of the emerging trends and the future trajectory of prompt engineering:
- Automated prompt optimization systems that test and refine prompts based on effectiveness metrics
- Hybrid approaches combining multiple frameworks for specific use cases
- Modular prompting with reusable components for efficient prompt construction
- Personalized frameworks adapted to individual user communication preferences
- Tool-integrated prompting blends reasoning with external capabilities
Prompting frameworks continue to evolve as our understanding of LLM capabilities advances. The choice of framework should be guided by specific use case requirements, balancing comprehensiveness with efficiency. As models become more capable, we can expect prompting frameworks to become more sophisticated, possibly evolving toward semi-autonomous systems that adaptively select the most appropriate techniques for each interaction context.
Whether you are developing applications, conducting research, or simply trying to get the most out of your AI interactions, understanding these frameworks provides a valuable toolset for effective LLM communication. The most skilled prompt engineers typically master multiple frameworks, switching between them fluidly based on context and requirements.
Do you want to elevate your AI strategy or integrate LLMs into your workflows? Our experts at Kuware can help you build more intelligent, faster, and more effective solutions. Reach out to Kuware today and start prompting with purpose.
