Prompt Engineering Tutorial

AutoGPTs are self-healing code generators

GPT-4 adapts automatically by evaluating, testing and improving the code. AutoGPTs automate entire cognitive workflows.

Photo by Brooke Lark on Unsplash

Introduction

Prompts programs — such as AutoGPTs¹, adapt code in fully-autonomous manner.

Large enterprises, such as Microsoft’s InferFix², are already using Autonomous Self-healing Software Systems³ to debug code internally. Self-healing workflows detect anomalies and diagnose errors automatically³.

Originally Naqvi et al.³ planned mainly data-driven approaches for Self-healing software. I see major development here.

Self-healing software requires now only Prompt engineering with LLM, such as GPT-4 or Claude.

The scope is not just debug of code through Fully-autonomous code generation.

LLMs enable automation of cognitive workflows.

The techniques are quickly developing: Self-Refine⁴, REDA⁵, BabyAGI⁸, HuggingGPT⁹, Generative Agents¹⁰, CAMEL¹¹ and SELF-DEBUGGING¹². AutoGPTs automate entire cognitive workflows. Prompts are the new programming interface, enabling automation of most cognitive workflows.

Self-healing code generation

Self-healing code generation refers to ability to automatically to generate code and debug it.

Let’s take a simple example.

I created a Python code, where I attempt to sum up two numbers and print the result. I added a bug to the code by not defining the num1-variable. Executing this code will print the following error in a Python interpreter.

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
/tmp/ipykernel_27/2367859671.py in <module>
      2 
      3 # Calculate the sum
----> 4 result = num1 + num2
      5 
      6 # Print the result

NameError: name 'num1' is not defined