THESE prompts make NEW ChatGPT o1 UNSTOPPABLE! (Insane Results)

Chad GBT's New Model: Five Unstoppable Prompts

Introduction to the New Model

• Chad GBT released their new 01 model, which offers deeper connections and tailored responses compared to previous models like GPT-4 or Claude.

• The video aims to present five prompts that enhance the performance of this new model, emphasizing its unique capabilities.

AI Foundation Community

• The AI Foundation Community is highlighted as a resource for mastering efficiency and productivity with AI, featuring 337 members sharing insights and feedback.

• The community includes a classroom with various courses on mastering AI and hosts four live calls per week.

Distinct Features of the New Model

• This model utilizes reinforcement learning for complex reasoning, allowing it to think before answering.

• It employs long internal Chain of Thought prompting, ensuring responses are well-mapped and tailored to user problems.

Brainstorming Paradigm Prompt

Overview of the Prompt Structure

• The first prompt introduced is a brainstorming paradigm consisting of seven steps designed for in-depth problem-solving.

• This prompt allows users to input detailed information, leading to more customized responses than those from GPT-4.

Steps in the Brainstorming Process

1. Define Problem & Role

• Users must list their problem while assigning a role for the model to adopt, enhancing its contextual understanding.

2. Brainstorm Solutions

• The model generates three potential solutions while considering factors influencing each choice.

3. Probability Evaluation

• Each solution receives a probability score (1%-100%) based on its likelihood of success along with pros and cons analysis.

4. Isolate Best Solution

• After evaluating probabilities, the lowest two scores are excluded to identify the best solution among those proposed.

5. Iterative Improvement Loop

• Users repeat steps by reintroducing their original problem alongside the winning solution for further brainstorming on potentially better alternatives.

6. New Probability Evaluation

• A fresh evaluation is conducted on newly generated solutions from step five using similar criteria as before.

7. Final Winner Isolation

Generating Winning Solutions with AI

Iterative Solution Generation

• The process involves generating two winning solutions and repeating the loop (steps 5-7) three more times to refine results, focusing on isolating winners and excluding losers.

Role Prompting for Product Development

• The speaker discusses using role prompting by defining a specific role (e.g., professional product developer) to guide the AI in brainstorming unique product ideas, particularly in e-commerce.

Internal Processing of AI Responses

• After inputting a detailed prompt into ChatGPT, the response time may increase as the model evaluates options and formulates solutions through an internal chain of thought prompting technique.

Evaluation of Solutions

• The AI takes time to analyze before responding, ensuring that it provides well-thought-out answers. It begins by reiterating the problem and brainstorming potential solutions while evaluating their probabilities of success.

Brainstorming Loop Iteration

• The model initiates a three-step brainstorming loop, providing probability evaluations for each solution generated during its iterations, showcasing its analytical capabilities compared to other models.

Drawing Connections Between Subjects

Consolidating Information from Research

• A simpler prompt is introduced to draw connections between two subjects based on provided research. This approach aims to consolidate complex information effectively.

Chain of Thought Prompting White Paper

• The speaker references a white paper on chain of thought prompting released in January 2023, highlighting its implementation for improved reasoning within large language models.

Copy-Pasting Research into AI

• To utilize extensive research material effectively, the speaker demonstrates copying content from a white paper into ChatGPT while posing questions about how chain of thought prompting enhances large language models' performance.

Exploring Revolutionary Concepts

• By asking how chain of thought prompting is revolutionary when used internally in large language models, the speaker emphasizes exploring innovative concepts through comprehensive prompts.

Anticipation for Future Capabilities

Revolutionizing Reasoning in Large Language Models

Advancements in Reasoning Capabilities

• The recent advancements discussed highlight how immediate reasoning and chain-of-thought prompting enhance model comprehension, making it revolutionary for large language models.

• This approach allows models to generate internal reasoning steps without explicit user prompts, mirroring human thought patterns and enabling more effective problem-solving.

• The speaker emphasizes the importance of drawing deeper connections between concepts rather than just providing answers, which can be approached philosophically or hypothetically.

Explaining Thought Processes

• A key use case involves asking the model to explain its reasoning behind an answer, fostering a sustainable understanding of its processes.

• Unlike other models, this new capability enables the model to reflect on its previous thought processes before delivering responses, enhancing depth and insight.

Practical Application: Content Calendar Example

• An example is provided where the speaker requests a content calendar for their YouTube channel using data from past videos as context.

• The initial prompt includes specific goals and context, leading to video ideas generated by the model after 24 seconds of processing time.

Insights from Model's Reasoning

• Following up with a request for explanation reveals how the model analyzes past performance and identifies successful content themes based on observations made during analysis.

• Key observations include that full guides perform well and that new features attract viewer interest; these insights are crucial for tailoring future content effectively.

Backcasting for Goal Achievement

• The discussion transitions to backcasting as a method for developing actionable plans towards achieving both small and large goals using this advanced model.

Strategic Planning Using Backcasting with Large Language Models

Defining Your Desired Future State

• The first step in strategic planning is to define your desired future state, regardless of the goal—be it fitness, financial, or personal achievements.

• Utilize large language models (LLMs), particularly the 01 preview, to assist in creating a strategic plan through backcasting—a method that works backwards from the desired outcome.

Current State Analysis

• Conduct a current state analysis by assessing where you currently stand relative to your goals. This includes metrics relevant to your objectives (e.g., weight loss stats like height, weight, body fat percentage).

• Providing context and detailed information about your current situation is crucial for LLMs to generate effective milestone development and pathways towards achieving your goals.

Milestone Development and Pathways

• After defining both future and current states, LLMs will identify key milestones necessary for progress. You can specify timelines for these milestones (e.g., 90 days).

• For example, if aiming for a community growth goal (like reaching 3,000 members), provide specific details about what you want this community to achieve and how it should function.

Generating Strategic Plans

• Once the model has all necessary inputs (desired state, current state), it will work backwards to create a comprehensive strategic plan tailored to your needs.

• The output includes month-by-month goals and tasks required to maintain focus on achieving the overall objective while also outlining necessary conditions for success.

Action Plan Breakdown

• The generated action plan is highly detailed; it breaks down immediate actions as well as short-term and long-term strategies.

• It emphasizes analyzing churn reasons through feedback collection and improving onboarding processes so new members feel welcomed—demonstrating a thorough approach in addressing potential challenges.

Enhancing Writing with New Models

Enhancing AI Responses: A Strategic Approach

Reviewing and Improving AI Outputs

• The process involves instructing the AI to review its previous answer, aiming for a more refined response. This method enhances existing writing by prompting the AI to explain its changes.

• A simple prompt is suggested: "Review your last response and search for areas of improvement." This encourages the AI to analyze its prior output critically.

• The AI evaluates its previous response, focusing on clarity, error correction, and potential adjustments in milestones and action plans.

• Key improvements identified include refining milestones, detailing reduction strategies, and expanding marketing strategies. This shows that the follow-up prompts can lead to significant enhancements in strategic planning.

• After revisions, the AI highlights changes made in bold text. It demonstrates how effective prompts can yield tailored solutions for goal-setting initiatives or strategic plans.

Community Engagement and Learning

• The speaker invites viewers to engage with their community focused on artificial intelligence learning. They emphasize building real human connections beyond just knowledge acquisition.

• Weekly calls are organized for new members to introduce themselves, fostering personal relationships within the community while discussing AI topics.