Search anything:

OpenAI Codex: A Technical Overview: Model behind LLM

Binary Tree book by OpenGenus

Open-Source Internship opportunity by OpenGenus for programmers. Apply now.

Ever since the advent of modern day computing, there has been a steady demand for people who can read, understand and write code in various languages for the purpose of building products in technology. The demand been so consistent that a new avenue of engineering itself has opened up in response to t namely, Software Engineering. Contrary to other branches of engineering however, one key element that makes it stand out is the presence of a human element which is more pronounced compared to other branches owing to large scale automation in them. However- by leveraging tools of Artificial Intelligence, we are moving closer to reversing this trend of having a low degree of automation in Software Engineering one step at a time and one such tool is the OpenAI Codex.

The aim of this article at OpenGenus shall be to facilitate an understanding of the concepts and principles behind the OpenAI Codex, it's applications and it's implementation for use by a layperson.


OpenAI Codex is an artificial intelligence platform that can write code in response to natural language prompts. It is a type of GPT (Generative Pre-trained Transformer) model that has been trained on a massive amount of code and natural language data. Owing to the large amount of data it has been trained on, it can write code in multiple languages such as Python, C++, Javascript and so on.

Given the value it can potentially bring to technologically intensive organizations, it is a breakthrough technology that has the potential to revolutionize software engineering as a whole.

Working Principles behind the OpenAI Codex

Code Generation

It is a method for generating a random sequence of tokens depending on the context vector. Similar to OpenAI's renowned language model GPT-3, The OpenAI Codex is a sort of GPT (Generative Pre-trained Transformer) model,pre-trained on a variety of codebases, including open-source repositories, Stack Overflow postings, and programming textbooks.

In order to generate code, it employs a variety of neural networks, including transformer networks and LSTM (Long Short-Term Memory) networks. The transformer network encodes natural language input and generates a context vector, which is subsequently sent to the decoder network. The context vector is used to generate the code by the decoder network.

It also employs a number of techniques, including beam search - a search algorithm used to find the most likely sequence of tokens to generate syntactically and semantically correct code - and sampling - a technique used to generate a random sequence of tokens based on the context vector for code generation.

Code Refactoring

OpenAI codex also has the abillity to suggest changes to a piece of code to make it more optimized in terms of time and space complexity, as well as readabillity and reproducabillity. To this end, it uses a combination of machine learning models and natural language processing to provide suggestions to optimize the code it is passed. The entire process can be summarised as follows.


  • Language Understanding-
    NLP techniques are used to understand the programming language, syntax, and context in which the code being passed is written.
  • Machine Learning Models-
    Using machine learning models that have been trained on a vast corpus of code. methods to optimize the code based on the patterns and structures they have learned in the previous stage are discovered.
  • Code Analysis-
    In this phase, the codex identifies potential areas for improvement. This includes identifying redundant or inefficient code, suggesting better algorithms or data structures, and finding ways to improve the performance of the code.
  • Contextual Suggestions-
    The codex now generates suggestions that are specific to the context of the code. For example, it can suggest optimizations based on the specific hardware or software environment in which the code will be running.
  • Natural Language Responses-
    The generated suggestions are then presented to the user in natural language.

Code Review

Apart from code generation and refactoring, the codex can also analyze any existing code for bugs or faults. It reviews the code that it is passed using a combination of natural language processing (NLP) and machine learning approaches.

Firstly, the codex analyses the input code and converts it into a semantic representation using NLP. Then, it analyses said semantic representation using several machine learning models to find any potential flaws or faults in the code.

The ML models used are trained on a vast corpus of code, allowing them to identify typical coding patterns and potential errors based on those patterns. Furthermore, a type inference system is utilised to determine the kinds of variables and functions in the code, which aids in the identification of possible errors such as type mismatches or incorrect function calls.

Using the OpenAI Codex

Now that we are familiar with the OpenAI codex to an extent, let's understand how we can use it.

The OpenAI API provides a simple interface that allows anyone to send natural language prompts to the AI model and receive code in response. Here is an example of how a layperson can use OpenAI Codex to generate code.

For this task, there are three steps involved.

Sign up for the OpenAI API: To use the Codex, you need to sign up for the OpenAI API first, which can be done by visiting the OpenAI website and following the instructions written there. In the end, you shall be provided with an API key.

Formulate your prompt: Formulate your prompt that describes what you want the program to do in natural language .

Send your prompt: Send your prompt to the OpenAI API using your preferred programming language, such as Python.

Before running the code below, ensure that the openAI library is installed on your development environment.

import openai
openai.api_key = "YOUR_API_KEY"
model = "text-davinci-002"
response = openai.Completion.create(
code = response.choices[0].text

If this code successfully runs, the codex will generate the code for you based on your natural language prompt. The generated code will be returned as a string that you can then copy and paste as per your requirements.

Tools based on OpenAI Codex

Below listed are some tools which leverage the OpenAI Codex.

  • Copilot- It is an AI-powered pair programmer based on OpenAI Codex, developed by OpenAI in collaboration with Github. It offers autocomplete-style suggestions as the user codes.
  • GPT Neo- It is an open-source project that aims to replicate the performance of GPT-3 using only open-source tools and resources. GPT Neo uses OpenAI Codex as one of its primary components and has achieved impressive results in natural language processing and code generation tasks.
  • CodeXGLUE- It is a benchmarking platform for evaluating the performance of different language models on various natural language processing tasks. CodeXGLUE includes tasks such as code generation, code retrieval, and code completion, all based on the OpenAI Codex.

Questions to Consider

Q1) In the context of code refactoring, is Code Analysis same as Language Understanding? If not, how?

No. Code Analysis involves understanding the language the code was published in while Language Understanding is an analysis of the possible ways to optimize the code.
No.Language Understanding involves understanding the language the code was published in while Code Analysis is an analysis of the possible ways to optimize the code.
Can't Say.
Yes, they are not the same. Language Understanding involves understanding the language the code was published in while Code Analysis is an analysis of the possible ways to optimize the code.

Q1)Mention the methods used by the OpenAI Codex in code generation?

Web Crawling
Transformer Networks
Both Transformer Networks and LSTMs.
Both Transformer Networks and LSTMs are employed by the OpenAI codex to generate code, among others.
OpenAI Codex: A Technical Overview: Model behind LLM
Share this