[AI] GPT‐Engineer - vinhtbkit/bkit-kb GitHub Wiki

Why GPT-Engineer?

  • The steps to build an application: selecting a framework, choosing technologies, databases, etc. You might even write some basic APIs like CRUD.

  • Let's assume we are building a REST API application using the Spring Boot framework. The construction steps could be as follows:

    • use maven archetype to create Maven project templates.
    • add dependencies
    • create a docker-compose file to build and run services that serve the purpose of running code
    • add the necessary configurations to the application
    • use liquibase to create database schemas
    • create some basic CRUD APIs.
    • ...

  • Typically, developer will take around 4-6 hours to complete the above tasks, requires a significant effort and time-consuming. Additionally, when building a microservices system, we may have to copy and then remove unnecessary dependencies, resulting in a time-consuming process for these scratch tasks.

  • Instead of doing tasks like the ones above, with GPT-Engineer:

    • you no longer need to tediously write code from scratch. Instead, you can describe your project, and the system will generate the entire codebase for you.
    • It eliminates the need for copying and pasting code snippets, creating files manually, or configuring project environments.

Introduction

  • GPT-Engineer is a project that uses GPT-4 to automate the process of software engineering. It includes several Python scripts that interact with the GPT-4 model to generate code, clarify requirements, generate specifications, and more.
  • With GPT-Engineer, developers no longer need to start coding from scratch or spend hours searching for relevant code examples. By providing a clear and concise prompt that describes the desired project or functionality, developers can receive a comprehensive codebase tailored to their specific requirements.
  • Advantages
    • Time and Effort Savings
    • Improved Productivity and Efficiency
      • GPT-Engineer empowers developers to work more efficiently, as it takes care of routine coding tasks. With the generated codebase, developers can accelerate the development process, iterate faster, and allocate their time to critical aspects of the project, such as design, testing, and optimization.
    • Enhanced Code Quality
      • The code generated by GPT-Engineer is based on best practices and standards observed in the software development community. This ensures that the generated codebase is of high quality, well-structured, and adheres to coding conventions, reducing the chances of introducing errors or vulnerabilities.
    • Learning and Exploration
      • GPT-Engineer serves as a valuable learning tool for developers. By examining the generated code, developers can gain insights into different coding techniques, design patterns, and best practices. This exposure to diverse codebases can expand their knowledge and foster creativity in solving programming challenges.
  • Disadvantages
    • Build failures may occur.
    • Domain-specific Knowledge
      • GPT-Engineer operates based on pre-trained models and may not have expertise in specialized domains. Developers should exercise caution when using it for highly specific or complex projects that require deep domain knowledge.
    • Code Validation and Testing
      • The generated code should undergo rigorous testing and validation to ensure its functionality and robustness. Developers must thoroughly review and test the codebase, making necessary modifications and improvements as required.
    • Dependency Management
      • GPT-Engineer may not consider the specific dependencies or frameworks preferred by individual developers. It’s crucial to review and configure the generated codebase to align with the desired development environment and preferred tools.

Installation and Setup

  1. Create new secret key
  1. Installation GPT-Engineer
git clone https://github.com/AntonOsika/gpt-engineer.git
cd gpt-engineer
pip install -e .
brew install python-tk

Demo

How to approach GPT-Engineer

  • Have knowledge about OpenAI.
    • Model
    • Text generation
    • Prompt engineering
    • Function calling (optional)
    • Embeddings (optional)
    • Fine-tuning (optional)
  • Have basic knowledge of Python.

Prompt engineering

Six strategies for getting better results:

  • Write clear instructions
    • Include details in your query to get more relevant answers
    • Ask the model to adopt a persona
    • Use delimiters to clearly indicate distinct parts of the input
    • Specify the steps required to complete a task
    • Provide examples
    • Specify the desired length of the output
  • Provide reference text
    • Instruct the model to answer using a reference text
    • Instruct the model to answer with citations from a reference text
  • Split complex tasks into simpler subtasks
    • Use intent classification to identify the most relevant instructions for a user query
    • For dialogue applications that require very long conversations, summarize or filter previous dialogue
    • Summarize long documents piecewise and construct a full summary recursively
  • Give the model time to "think"
    • Instruct the model to work out its own solution before rushing to a conclusion
    • Use inner monologue or a sequence of queries to hide the model's reasoning process
    • Ask the model if it missed anything on previous passes
  • Use external tools
    • Use embeddings-based search to implement efficient knowledge retrieval
    • Use code execution to perform more accurate calculations or call external APIs
    • Give the model access to specific functions
  • Test changes systematically
    • Evaluate model outputs with reference to gold-standard answers

References