Code Generation LLM's and how businesses are using these for improving productivity

Today's article in on a very unique topic of Code Generation LLM and how businesses are using these LLMs for improving productivity.

As part of this article, I will cover 1) What are code generation LLMs 2) What is the need for these LLMs and 3) How businesses are using them to improve productivity - I will cover some use case here where code generation LLM can be used.

So, let's dive in.

What are code generation LLMs.

Code generation LLM's are LLMs which generate programming language code. Code generation LLM can do code syntax checking, address Fill-in-middle in code, improve or optimize already existing code (written by programmers) and finally answer questions on code.

But how does Code Generation LLMs can do all the above.

Answer lies in the way LLM are trained on code.

Fundamentally two types of LLM training on code is there.

• Reasoning
• Non-Reasoning

Reasoning LLM's for code are DeepSeek R1. Non-Reasoning LLM's for code are Qwen2.5B, StarCoder, LLAMA 3 fine-tuned on code, Mistral Codestral to name a few.

Code Generation LLM are Causal LLM.

Let's cover what data LLM are trained on and also a standard training pipeline of how LLM's are trained on code.

LLMs are trained on code using following data/tokens.

• Github Code Snippets across 90-100 odd programming languages such as Python, C++, SQL etc.
• Github Comments and Documents
• Webscrapped Tokens relevant for code
• Text

Code generation LLMs have very long context.

Pre-training objectives for such code generation LLM is:

• Next Token Prediction
• Fill-In-Middle (FIM)

The training pipeline for a Code Generation looks like as below:

How does one code generation LLM differs from others is in two major aspects:

• Data
• Context Window - Longer the better for code generation

Having covered what code generation LLMs are let's cover second part of the article - Why do we need code generation LLMs

Code generation LLMs can majorly help in:

• Code Optimization for any programming language. This is otherwise a major time taking tasks
• Fill-In-Middle - Fill missing code between the start and end of code.
• Code Generation from scratch
• Unit Test Code Generation
• Code Syntax Enforcement

Let's now move to cover the final part of the article - How are businesses using Code Generation LLM to increase productivity.

Think from engineering code perspective, think from cost perspective to develop code and think from efficiency perspective of the code generated - This is where code generation LLM help to reduce feature releases TIME TO MARKET and GIVE BUSINESSES A COMP EDGE AT LOW COST.

Think from an Enterprise Platform Companies, B2C Platforms, D2C Platforms and think engineering, how much time/money /effort goes to develop code. You have your answer.

One important point though - Current Code Generation LLMs are good, and they are getting better however still log way to go.

In summary my assumption is that within a year from now code generation LLM will generate code which is equivalent to human generated code in terms of quality.

Thanks All. Hope you had a good read.

Disclaimer: Opinion / Views expressed above are the author's personal and has no bearing or affiliation to the authors current employer or any earlier/past employers.

Credit:

https://blog.fabrichq.ai/large-language-models-for-code-generation-f95f93fe7de4

https://magazine.sebastianraschka.com/p/new-llm-pre-training-and-post-training

Image Credit:

https://blog.fabrichq.ai/large-language-models-for-code-generation-f95f93fe7de4

https://magazine.sebastianraschka.com/p/new-llm-pre-training-and-post-training