Draft:Flow-of-thought prompting
Submission declined on 15 April 2025 by Gheus (talk).
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|  |
{{Infobox concept}}
Flow-of-Thought Prompting is a prompting methodology for artificial intelligence (AI) systems, where users freely express their intentions, context, and reasoning “out loud.” Unlike rigid templates or step-by-step instructions, Flow-of-Thought preserves the user’s authentic thought process, enabling AI models to better understand nuances, tone, and goals.[1]
Definition
[edit]Flow-of-Thought Prompting is based on the idea that users do not need to follow a strict “recipe” when prompting an AI, but can write out their thought process as an internal monologue. The goal is to fully leverage large language models’ ability to interpret context, sentiment, and subtle cues.[1]
Core Principles
[edit]- Natural Expression: Use a conversational tone that reflects how you actually speak or think.
- Rich Context: Provide all relevant details—constraints, preferences, background—to deepen AI understanding.
- Clear Intention: Be transparent about what you want to achieve, even if it’s loosely defined.
- Open-Ended Discovery: Encourage exploration of ideas without imposing rigid constraints upfront.
- Collaborative Tone: Treat the AI as a partner or sounding board, not just a tool.
How It Works in Practice
[edit]Flow-of-Thought Prompting generally follows these steps:
1. **Start with Your “Why”:**
For instance, “I’m thinking about creating a new marketing strategy because my current approach feels stale...” By sharing your motivation or problem, you establish context and intention.
2. **Add Personal Perspective:**
Mention relevant backstory, constraints, or preferences, e.g., “I love direct storytelling and wonder if that should be the heart of my campaign.” This gives the AI richer insight into your unique angle or situation.
3. **Propose Questions or Hypotheses:**
For example, “Does focusing on a narrative-driven approach resonate with my 20–30 audience?” Here, you highlight uncertainties and invite specific guidance or data from the AI.
4. **Invite the AI’s Guidance:**
Ask openly: “Let me know if I’m missing something or if you have extra insights...” This collaborative tone encourages the AI to offer creative or data-driven input, rather than just a direct answer.
Example Prompt
[edit]“I’ve been trying to communicate my community-building mission in a more authentic way. My Instagram posts feel forced. I want a natural storytelling angle that reflects my real values. Most of my audience is in their 20s–30s and cares about sustainability. Do you have any ideas on how to tie my day-to-day life into this message?”
Benefits
[edit]- Enhanced Relevance: When you share your thought process, the AI can tailor responses more accurately.[2]
- Boosted Creativity: Free-flow expression often sparks new ideas that might not surface with a rigid prompt.
- Reduced Prompt Anxiety: No need for a “perfect prompt”; authenticity is more valuable than perfection.
- Scalable to Any Context: Suitable for marketing, content creation, educational use, trip planning, etc.
- Builds Confidence & Ownership: Users feel like co-creators rather than mere recipients of AI output.
Scientific Support & Evidence
[edit]Evidence from AI & Language Model Research
[edit]- Studies on “chain-of-thought” prompting show that large language models perform better on complex tasks when guided by intermediate reasoning steps.[3]
- Research by Zhou et al. (2023) underlines how prompt style alone can significantly affect model performance, reinforcing the value of flexible, context-rich prompts.[2]
Evidence from Cognitive Science & Psychology
[edit]- Ericsson & Simon (1993) demonstrated that “think-aloud” protocols capture genuine thought processes without degrading performance.[4]
- The “rubber duck debugging” method illustrates how explicitly articulating one’s reasoning can aid in identifying errors and clarifying ideas.[5]
Evidence from Creativity Research
[edit]- Freewriting research suggests that unstructured expression enhances creativity by bypassing self-criticism.[6]
- Csikszentmihályi’s “flow state” concept connects open-ended creative expression to heightened innovation and enjoyment.[7]
Evidence from HCI & Communication Theory
[edit]- Reeves & Nass (1996) found that people tend to treat computers like human conversational partners, reinforcing the effectiveness of a natural, conversational approach.[8]
- Clark & Brennan’s “grounding” principle emphasizes the importance of shared context in dialogue; similarly, a rich and open prompt helps the AI better grasp user intent.[9]
Comparison with Chain-of-Thought Prompting
[edit]| Aspect | Chain-of-Thought Prompting | Flow-of-Thought Prompting |
|---|---|---|
| Core Focus | Detailed logical steps for accuracy in complex tasks. | Natural, free-flow “thinking out loud,” emphasizing context and authenticity. |
| Structure | Breaking down problems step by step. | Open-ended, narrative style akin to an internal monologue. |
| Goal | Maximizing logical clarity & correctness. | More creative, personalized, or context-heavy responses. |
| Typical Usage | Math puzzles, systematic coding, logic-intensive queries. | Brainstorming, branding, personal development, exploratory thinking. |
| Emphasis | Analytical problem-solving & explicit reasoning chains. | Authenticity, emotional nuance, user’s broader perspective. |
| Key Benefits | Reduces errors in multi-step calculations. | Encourages creative flexibility and provides deep context for targeted answers. |
References
[edit]- ^ a b Flow-of-Thought Prompting Framework (2023). Official introduction and definition. Available at: https://flowofthoughtprompting.com/
- ^ a b c Zhou, P., Li, J., & Wang, X. (2023). "A Survey on Prompt Engineering for Natural Language Processing." arXiv:2302.11382. [1]
- ^ a b Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." Proceedings of the 39th International Conference on Machine Learning. [2]
- ^ Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis: Verbal Reports as Data. MIT Press.
- ^ Severance, C. (2012). "Rubber Duck Debugging in Software Education." ACM Inroads, 3(2), 32–37.
- ^ Elbow, P. (1973). Writing Without Teachers. Oxford University Press.
- ^ Csikszentmihályi, M. (1990). Flow: The Psychology of Optimal Experience. Harper & Row.
- ^ Reeves, B., & Nass, C. (1996). The Media Equation. Cambridge University Press.
- ^ Clark, H. H., & Brennan, S. E. (1991). "Grounding in Communication." In Perspectives on socially shared cognition, American Psychological Association.
Bibliography
[edit]- Elbow, P. (1973). Writing Without Teachers. Oxford University Press.
- Csikszentmihályi, M. (1990). Flow: The Psychology of Optimal Experience. Harper & Row.
- Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis: Verbal Reports as Data. MIT Press.
- Reeves, B., & Nass, C. (1996). The Media Equation. Cambridge University Press.
External Links
[edit]Category:Artificial intelligence Category:Natural language processing Category:Prompting methodologies Category:Human–computer interaction
- in-depth (not just passing mentions about the subject)
- reliable
- secondary
- independent of the subject
Make sure you add references that meet these criteria before resubmitting. Learn about mistakes to avoid when addressing this issue. If no additional references exist, the subject is not suitable for Wikipedia.