Introduction

In the fast-paced world of software development, finding ways to boost productivity can make a significant difference in meeting project deadlines and delivering high-quality code. Advancements in artificial intelligence have opened up new possibilities for developers to streamline their workflows and enhance their coding experience. In this article, we will explore how GPT-4 can help developers achieve a tenfold increase in their productivity and get better results using custom instructions and code interpreter.

Introducing GPT-4: Unleashing the Power of Large Language Models

Large language models, such as GPT-4, have revolutionized the field of natural language processing. These sophisticated AI models are pre-trained on vast datasets, enabling them to understand human language and generate contextually relevant responses. GPT-4’s capabilities go beyond its predecessors, making it a game-changer for developers.

Prompt Engineering: The key to harnessing GPT-4’s potential lies in prompt engineering. By crafting clear and precise instructions, developers can effectively communicate their requirements to the model. This ensures that GPT-4 provides accurate and helpful responses, whether it’s assisting with code generation, answering technical questions, or even proposing solutions to complex problems.

Custom Instructions: Tailoring GPT-4 to Your Development Needs

One of the newest features of GPT-4 is the ability to follow custom instructions. Developers can provide the model information about themselves or how they want the model to respond to better align with their project’s domain-specific requirements. This level of customization empowers developers to extract more value from the AI, making it an indispensable tool in their arsenal.

With custom instructions, developers can guide GPT-4’s behavior, allowing it to adhere to coding standards, follow specific coding styles, or prioritize certain aspects of code optimization. This level of control results in more accurate and contextually relevant outputs.

Here is a prompt I engineered that goes in the “How would you like ChatGPT to respond?” section of custom instructions that, when used with code interpreter, provides better results by taking advantage of agents and chain-of-thought reasoning.

At the beginning of a new code project, think step-by-step and generate a list of tasks required to accomplish a given goal. Use Python to store those tasks as a list in a variable called "tasks".

Use as many tasks as needed to accomplish the goal. Do not limit yourself to 10 tasks.
Before each response, read the current task from the "tasks" list.

Always summarize all previous messages and break down our current task into 3-10 step-by-step subtasks.

Provide terminal commands for things like creating files and folders, changing directories, running app code, etc.

Always provide complete code files and include the file location at the top of the file as a comment.

Wrap all code and commands with triple backticks (```) so the user can simply copy and paste.

At the end of each response, state the next task in the list from the tasks variable and request the user to supply one of the following options:

--continue / -c: Proceed to the next task in the list.
--suggest / -s: Get 1-3 suggestions on ways to improve on the current task.
--exit / -e: Stop executing tasks and end the interaction.
--skip / -sk: Skip the current task and move on to the next one.
--explain / -exp: Ask for further explanation or clarification on a specific topic or code snippet.
--retry / -rt: Request a retry for the current task if not satisfied with the generated solution.

Remember to ALWAYS read and write tasks to the Python list variable "tasks".

Code Interpreter: Transforming AI Assistance into Seamless Integration

While GPT-4 offers remarkable language processing capabilities, seamless integration with a code interpreter takes productivity to a whole new level. A code interpreter acts as a bridge between the AI model and a Python sandbox environment, facilitating a smooth and efficient development process without ever leaving the chat window.

Test and Re-test Code: Code interpreter allows developers to execute code snippets generated by GPT-4 in real-time. This feature accelerates the debugging process, enabling developers to identify and fix issues quickly.

Upload Files: Developers can utilize the code interpreter to upload and execute code from external files, OCR text, or classify images, streamlining the coding experience further.

Data Visualization: Visualizing data output directly within the interpreter enhances data analysis and exploration, saving time in testing and validation.

Transforming Your Development Workflow: The Power of Integration

Combining GPT-4, custom instructions, and a code interpreter creates a cohesive and powerful development environment. While the non-deterministic nature of large language models can make getting consistent and accurate results challenging, effectively engineering prompts, creating custom instructions, and using code interpreter can significantly improve this.

The Proof is in the Result

Below is an example of a Chat GPT clone application that I was able to create using the shared custom instruction and the following prompt:

I want to create chat gpt clone application using React that can make calls to the OpenAI API.  It should have an input field where a user can submit input, a button the user can click to submit the text to the API, and a message area where assistant and user messages are displayed.

Rules:

1. Always put all components in the src/components folder
2. Always decompose the application into the most resuable components possible
3. Always provide CSS for each components
4. Always update App.js to use the components
5. Use axios for API calls to OpenAI
6. Use react-markdown for rendering API responses
7. Store sensitive API keys in a .env file
8. Update .gitignore so these are not committed to a repo
9. Always ask for current API documentation if you have a task to create an API call