Auto-generate python code from simple natural language by leveraging the world’s most advanced language model.
Please go through the below articles in the same order to connect the dots and understand the key tech stack behind Python Code Assistant- an application powered by GPT-3:
FastAPI — The Spiffy Way Beyond Flask!
Streamlit — Revolutionizing Data App Creation
A Brief Introduction to GPT-3
While creating any GPT-3 application the first and foremost thing to consider is the design and content of the training prompt. Prompt design is the most significant process in priming the GPT-3 model to give a favorable and contextual response.
As a rule of thumb while designing the training prompt you should aim towards getting a zero shot response from the model, if that isn’t possible move forward with few examples rather than providing it with an entire corpus. The standard flow for training prompt design should look like: Zero Shot → Few Shots →Corpus based Priming.
For designing the training prompt for the python code assistant application, I have used the following structure for the training prompt:
Description: An initial description of the context about what the code assistant is supposed to do and adding a line or two about its functionality.
Natural Language (English): This component includes a minimal one-liner description of the task that will be performed by the code assistant. It helps – GPT-3 to understand the context in order to generate proper python code.
Code: This component includes the python code corresponding to the English description provided as an input to the GPT-3 model.
Author: Shubham Saboo
Full tutorial available at https://pub.towardsai.net/python-code-assistant-powered-by-gpt-3-dfecf1945925