Advanced AI Prompting Techniques for Better Reasoning and Creative Problem Solving

Table of Contents

• Overview of the Advanced Prompting Technique
• Step-by-Step Walkthrough of the Prompting Flow
• Automating the Prompting Process with Python
• Demo of the Technique on a Complex Reasoning Problem
• Conclusion and Next Steps

Overview of the Advanced Prompting Technique for Improved AI Reasoning

In this blog post, we will explore an advanced prompting technique that can help improve reasoning and common sense capabilities in large language models like ChatGPT. The technique involves chaining together a sequence of carefully designed prompts and looping back to iteratively refine solutions to complex problems.

At the core of this method is the 'tree of thought' principle for systematic prompting. By breaking down the reasoning process into smaller steps and repeatedly branching out ideas to explore multiple chains of logic, we can guide the AI through the key aspects needed to reach sound conclusions.

Prompt Chaining and Looping Concept

The first key concept is prompt chaining. This refers to creating a logical sequence of prompts, where the output from one prompt is fed as input to the next. For example, the first prompt defines the problem, the second generates proposed solutions, the third evaluates those solutions, and so on. The second concept is prompt looping. After generating and evaluating a set of potential solutions, we take the top rated solution, 'loop back' to an earlier prompt, generate additional solutions to compete with it, evaluate again, and repeat this iterative refinement process. By chaining prompts together into a full workflow, and looping back to continually pit solutions against each other Darwinian-style, this technique aims to systematically home in on the most viable response to complex challenges.

Following the Tree of Thought Principle

The tree of thought principle structures the prompting sequence similar to branching logic trees. The initial problem prompt acts as the base 'trunk'. Solution ideation prompts then split off like branches, exploring different chains of reasoning. Further prompting continues to sub-divide ideas and evaluate feasibility. Over multiple cycles of looping back up the tree and splitting into new branches, prompting progressively narrows down the strongest 'limb' representing the optimal solution. This leverages the AI's creative ideation strengths while also overcoming limitations in its reasoning abilities.

Step-by-Step Walkthrough of the Advanced Prompting Flow

Now let's go through a detailed step-by-step walkthrough of how this prompting technique works and flows together...

We'll break it down into the key stages, including: defining the initial problem, brainstorming solutions, ranking solutions, excluding weaker ideas, looping back to refine the best solution, and finalizing the winning idea.

Defining the Initial Problem Prompt

The first prompt clearly states the problem that needs to be solved. This grounds the entire subsequent prompting flow by establishing the initial 'trunk' at the base of the tree of thought. For example: 'My problem is that I want to break up with my girlfriend of 6 years as I feel we have grown apart, but I want to find the best approach for telling her.'

Brainstorming Initial Solution Ideas

The second prompt asks the AI to brainstorm 3 distinct solutions to the defined problem. For example, potential break up approach ideas like: direct communication, gradual distancing, mutual decision, etc. Ideation at this stage aims to explore different angles and logical chains of thought, before evaluating feasibility.

Evaluating and Ranking the Solutions

The third prompt asks the AI to analyze each proposed solution, evaluate how suitable it would be for solving the defined problem, and rank the options from 1 to 100 based on probability of success. This acts as the first filtering stage, identifying the strongest early 'branch' in the tree to further refine.

Excluding Lowest Ranked Ideas

The fourth prompt removes the two lowest ranked options, and asks the AI to rewrite only the highest rated solution idea, including its scored probability. Narrowing down to one idea streamlines the following refinement loops to build out that specific chain of reasoning.

Looping to Refine the Best Solution

In the fifth prompt, we loop back to request two new solution ideas to compete with the remaining top idea. After re-stating the initial problem for context, the AI is asked to re-generate alternate options and rank against the leading idea. This process repeats for 5 cycles, requiring the top idea to continually defend its position as the optimal approach.

Refining the Final Winning Idea

After many rounds of 'survival of the fittest' filtering, the emerging winning idea undergoes a final prompt to further develop the specifics of how it would be implemented. This includes thinking through scenarios, required steps, potential obstacles and responses. The resulting output of this last prompt in the chain offers a refined solution recommendation based on a structured reasoning process.

Automating the Prompting Sequence for Easier Application

Manually executing such an elaborate multi-step prompting sequence with precise input and outputs between each stage is tedious and error-prone.

To unlock the potential of this technique, I set out to automate the full prompt chaining and looping flow in a Python script. This allows the core logic to be defined once in code, while enabling easy re-use across diverse reasoning challenges.

Coding the Prompt Chaining Logic in Python

The Python script encapsulates the full 7-stage prompt sequence we walked through earlier into modular functions that can call one another. For example, initial_prompt() handles defining the problem, brainstorm_ideas() generates solutions, rank_solutions() evaluates feasibility, and so on. Chaining functions together using their input and outputs emulates the manual prompt chaining process to direct the AI down specific logical pathways in its reasoning process.

Building a User Interface for Streamlined Use

Allowing non-coders to leverage this automation, I also created a simple web interface that wraps the Python prompt chaining script. Users can paste in problem statements, and initiate end-to-end runs with one click rather than complex code. Progress bars visualize the behind-the-scenes flow as the AI works through the chained prompt sequence to converged refined solutions. Results display prominently once ready.

Testing the Automated Prompting Technique on Complex Reasoning Problems

With tooling in place to ease execution, I set out to test this advanced prompting approach on some classically challenging common sense reasoning problems that have stumped previous AI systems.

The Clothes Drying Challenge

One such challenge that a leading AI recently failed is: 'If 5 clothes take 5 hours to fully dry in the sun, how long would it take for 30 clothes to dry?' The AI answered '30 hours', incorrectly assuming a simple linear relationship. The correct solution is still 5 hours when drying larger batches, as the clothes would be laid out simultaneously with adequate airflow.

Achieving the Optimal Solution Through Structured Prompting

Feeding this exact problem into my automated prompting framework resulted in far more sophisticated logic. By methodically breaking down the variables at play, challenging assumptions, and iteratively refining feasibility, the AI arrived at the right solution: 'Drawing all 30 clothes simultaneously with adequate airflow, the total drying time would still be 5 hours based on the initial benchmark observation.' This demonstrates the power of structured prompting to overcome innate reasoning limitations in language models. More broadly, it highlights the potential to augment AI capabilities through a combination of automated prompting systems and human-directed logic flows.

Conclusion and Next Steps for Further Explorations

In this article, we walked through an advanced technique to take AI problem solving to the next level using prompt chaining and looping. By codifying this prescribed methodology in an automated Python system, complex reasoning challenges that once stumped leading AI models can now be tackled effectively.

Streamlining interface tooling around these orchestration concepts empowers non-coders to leverage previously inaccessible capabilities as well. Empowered by structured prompting routines, language models exhibit remarkable strides in surpassing innate common sense inferencing deficits.

Looking ahead, operationalizing these learnings into production diagnostic systems could pave the way for on-demand AI reasoning augmentation. Initial results compel further research at the intersection of procedural prompting protocols and software automation to scale up transformative impacts on emerging language models.

FAQ

Q: How does prompt chaining improve AI reasoning?
A: Chaining prompts allows the AI to build on previous responses, refine ideas over multiple cycles, and mimic human logic and critical thinking more closely through this process.

Q: What is the tree of thought principle?
A: The tree of thought principle involves branching out ideas through iterative brainstorming while pruning poor solutions over time to arrive at well-reasoned final ideas.

Q: What does the prompting loop achieve?
A: The prompting loop allows new solution ideas to compete with existing ones, undergoing repeated cycles of evaluation and filtering to surface the very best idea through a survival of the fittest process.

Q: How long does it take to automate the process with Python?
A: The video demonstrates coding prompt chaining logic in Python and building a web UI for the process within minutes, allowing full automation for rapid, scalable application.

Q: What was the sample problem tested?
A: The technique was tested on a clothes drying challenge that stumped the GPT-4 AI, successfully getting to the right 5 hour logic through the advanced prompting approach.

Q: Where can I get resources to try this technique?
A: The creator has shared text prompts and membership access to 40+ videos on Python coding and UI development for advanced prompting techniques in the video description.

Q: What real-world uses are there?
A: This methodology can enhance AI reasoning for complex business problems, product design, writing, coding, mathematics and more - the possibilities are endless.

Q: Does this work with ChatGPT too?
A: Yes, the step-by-step walkthrough is shown using the ChatGPT interface - so this technique is applicable to it and other AI systems.

Q: Can I use this without coding skills?
A: The text prompts allow anyone to try this manually through ChatGPT. But coding knowledge enables fully automating it for scalable application.

Q: What are the limitations of this approach?
A: The AI can hit limitations as prompts repeat over multiple cycles. So some human refinement of prompts may be needed for really long loops targeting extremely complex problems.