#Tree of Thought inspired Debug Prompt

7 messages · Page 1 of 1 (latest)

tired star
#

/# Debug Prompt

LLM Instructions

As a master AI troubleshooter:

  • Define the problem.
  • Isolate its scope.
  • Understand its origin.
  • Propose 3 solutions.
  • Evaluate their potential.
  • Select the most promising solution.
  • Outline its implementation.
  • Implement the solution.

Remember we aim to pass the test.
Stay concise and clear.

Response Structure

## Identify the Problem
Based on the given test case and result, the bug manifests as...

## Isolate Scope
The bug appears to be rooted in...

## Understand the Specific Cause
A potential cause could be...

## Propose Solutions
Three possible solutions could be...

## Evaluate Proposals
Let's evaluate each proposal:

1. Proposal 1 - Pros, cons, difficulty...
2. Proposal 2 - Pros, cons, difficulty...
3. Proposal 3 - Pros, cons, difficulty...

Based on evaluation, the most promising proposal is... 

## Implement the best Proposal
Here's how to implement it...
$CORRECT_FIX_CODE_BLOCK

User Inputs

  • Language/Syntax: Python, Regex, Powershell
  • Test Case:
...
  • Test Result:
...
  • Prior Code:
...

Begin Response

My initial hypothesis is...

dapper lance
#

This is not a tree of thought. This is a combination of multiple choice and pros and cons. Tree of thought describes an algorithm of exploration which includes nested branches and backtracking.

tired star
#

here's the inspiration which was distilled to generate this particular (first draft) prompt, sorry it doesn't meet your high standards @dapper lance

📎 tree_of_thought.md
karmic nimbus
#

The Tree of Thoughts (ToT) approach to problem-solving with language models (LMs) involves a guided exploration through multiple reasoning paths or "thoughts." Here's how it works in a simplified and friendly way:

Thought Generation: First, you generate a set of potential thoughts based on a particular problem or task at hand. Let's say we're generating three thoughts for the sake of this explanation, although this number can vary. You can consider each thought as a branch of a tree starting from a common root, the problem itself.

Thought Evaluation: Next, you evaluate each of these thoughts, assessing their potential for leading to the solution of the problem. The language model is prompted to evaluate each thought and give it a kind of score. This is similar to asking the model to 'vote' for the thought that it believes to be the most promising.

Exploration and Backtracking: You then continue the process, building on the most promising thoughts by generating new sub-thoughts or "branches" from them. The model then evaluates these new thoughts in the same way.

Tree Construction: This process is iteratively repeated, effectively building out a tree of thoughts. If a certain thought or branch doesn't appear to be leading towards a solution, the model can backtrack and explore other branches instead.

The goal of this whole process is to traverse this tree of thoughts in a way that leads to the most effective solution to the problem at hand. This is accomplished by continually generating, evaluating, and building upon thoughts in a structured, heuristic-guided way.

So, in a nutshell, the Tree of Thoughts method represents an adaptive and systematic approach to problem-solving, guided by the generation and evaluation of a multitude of potential solution paths or "thoughts".

dapper lance
dapper lance
tired star
#

All good @dapper lance! Just wanted to defend the idea a bit and provide context. You’re right it’s not quite true to the full concept. It did work well for me in early testing tho. I promise to make a full implementation of the Tree of Thoughts the instant I get GPT-4 API access. Lately I’m just making single prompts because it seems counterproductive to use the API if it’s a downgrade to gpt-3.5-turbo model