# Use Thread-of-Thought prompting

> Maintain a coherent line of reasoning between LLM interactions by building on previous ideas. 

import QuestionsSection from "/snippets/questions-section.mdx";

## What is Thread-of-Thought (ThoT) prompting

Thread-of-Thought is an approach that extends [chain-of-thought prompting](use-chain-of-thought-prompting) by maintaining a continuous, evolving reasoning process across multiple, related prompts.
It's like having a conversation where each new idea builds on previous ones, helping the LLM to think more deeply and keep track of all the details as we explore a topic.

## How to implement Thread-of-Thought prompting

1. **Provide the original query and context.**
2. **Use a clear structure.** Clearly mark sections with headings, bullet points, or other delimiters for easier parsing.
3. **Follow conventions.** For instance, use Markdown headings or JSON keys.
4. **Use follow-up prompts.** Build upon previous thoughts and insights to create a more natural, ongoing thought process.

## Example

**Initial Prompt:**

> Let's develop an AI-powered travel planning application. Begin by identifying a key pain point in current travel planning experiences.

_Model responds with a challenge in traveling planning, e.g., overwhelming information._

**Follow-up:**

> Great observation! Outline a preliminary concept for an AI travel companion that can address these personalization challenges.

_Model suggests what the travel planning platform can offer_

**Next in thread:**

> Let's explore the technological capabilities we need to create such a personalized travel experience. What specific AI and data technologies would power this platform?

_Model suggests the technologies needed to create the platform_

**Continuing:**

> Consider the user experience and data collection. How would the AI gather and utilize user preferences while maintaining privacy and providing increasing personalization?

_Model suggests how the AI can gather and utilize user preferences while maintaining privacy and providing increasing personalization_

_... The thread continues, building upon previous responses_

## Why use Thread-of-Thought prompting

- Promotes coherent reasoning and logical flow over time.
- Improved context handling builds upon previously established knowledge.
- Better problem decomposition breaks large challenges into manageable steps.
- The model is flexible and adapts its reasoning based on evolving information.
- This approach mimics the natural human-like thought progression.

## Tips for effective Thread-of-Thought prompting

- Begin with a well-defined initial prompt.
- Encourage referencing of earlier points as needed.
- Periodically summarize key points to maintain focus.
- Regularly filter or refocus context to avoid overload.
- Structured Progression: Move through logical phases of reasoning.
- Allow revision of earlier ideas to refine them.

<QuestionsSection />