Transform Your LLM Output with This Revolutionary System Prompt!

Enhancing the performance and accuracy of language models like ChatGPT and Claude can seem daunting, but there’s a simple yet powerful system prompt that can help you achieve just that! This guide will break down the key insights from the video and illustrate the transformative trick shared by Maharshi.

The Core Concept: Understanding Through Contemplation 🤔

What is the Contemplator Prompt?

The heart of enhancing LLM accuracy lies in a unique system prompt known as the Contemplator Prompt. This prompt encourages models to engage in deeper self-questioning and reasoning processes, mirroring human thought. The core approach is to prioritize exploration over immediate conclusions, allowing for more nuanced and accurate responses.

• Example in Action: A classic riddle asks, “Sally has three brothers; each brother has two sisters. How many sisters does Sally have?” Without the prompt, the model might incorrectly state “two.” However, with the Contemplator Prompt activated, it correctly reasoned that Sally is one of the two sisters, leading to the accurate answer of one sister.

Surprising Insight: The Power of Structured Thinking 📊

Language models typically don’t think or reason like humans; they generate text responses based on pre-trained knowledge. However, integrating structured thinking through the system prompt results in more accurate answers. The prompt scaffolds the model’s response by guiding it through logical steps, leading to a final answer that isn’t just plausible but correct!

• Fun Fact: Using open-ended prompts can lead to increasingly complex responses, as seen when the riddle skews logic without proper structure.

Practical Application Tip:

When crafting your prompt, be explicit in guiding your model to contemplate rather than simply respond. Use phrases like, “Let’s think through this step by step,” to encourage deeper reasoning.

Crafting the Perfect System Prompt 🛠️

Key Elements of the Contemplator Prompt

1. Extensive Internal Monologue: Instruct the model to think aloud and explore options before arriving at a conclusion.
2. Output Format: Specify a clear structure for the final answer, such as using XML formats to avoid confusion in reasoning.

Example Structure:

Your response must follow this structure:
1. Contemplative thought process
2. Final answer

This setup enhances clarity and enables the reader to follow the model’s reasoning.

Implementing the Prompt in ChatGPT and Claude

ChatGPT:

• Go to the Settings ➡️ Personalization ➡️ Custom Instructions.
• Fill in sections with tailored instructions, including the clarity and structure outlined.

Claude:

• Access the option to create and edit style.
• Paste your custom prompt directly to define how Claude engages in reasoning.

Quick Tip:

In both interfaces, consider using condensed prompts for routine tasks (like generating jokes) while reserving detailed prompts for complex queries to maximize efficiency and minimize token usage.

Exploring Real-Life Examples 🚀

Logical Inquiry: A Classic Conundrum

When asked, “A building has its third floor above street level. How many stories are above ground?” without the prompt, the model might struggle. But with the Contemplator Prompt, the model examines implications clearly and concludes that there is indeed one story above ground — correctly deducing from the spatial puzzle.

• Real-world Application: Use this approach when faced with ambiguous queries to encourage precision in responses.

The Token Dilemma 🔍

While the Contemplator Prompt is powerful, it consumes a significant number of tokens, making it less viable for longer contexts. It’s essential to evaluate when to use a deep reasoning prompt versus a more straightforward response.

• Practical Strategy: Assess your query’s complexity; if it can be simplified, opt for shorter prompts to save tokens.

Building Your Resource Toolbox 📚

1. Maharshi’s System Prompt: GitHub Gist – A deep dive into the Contemplator system prompt.
2. Follow Maharshi on Twitter: Maharshi on X – Insights and updates on LLM applications.
3. Support Maharshi’s Channel: Patreon – For those wishing to contribute to the content.
4. Ko-Fi Donation Link: Ko-Fi – Another way to support the creator.

Additional Tools:

• Online Problem Solvers: Websites like Wolfram Alpha can serve as examples of logical structuring.
• Books on Logic and Reasoning: Helps in developing your own prompts and inquiry style.

The Path to Improved LLM Accuracy ✨

By implementing the Contemplator Prompt, users can unlock more accurate, thoughtful responses from language models. This approach empowers both casual users and developers to extract better insights and information. Instead of simply accepting the model’s outputs, encouraging thoughtful deliberation results in greater understanding and functionality. The transformative ability of this system demonstrates how structured prompts can redefine user experience with LLMs.

Inspiring Quote: “The greatest of all abilities is the ability to think.” — Unknown

Take these insights into your daily interactions with language models to elevate accuracy and depth of understanding!