Machine Learning Times
Machine Learning Times
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8 months ago
How to Build An Enterprise LLM Application: Lessons From GitHub Copilot

Originally published in The GitHub Blog, Sept 6, 2023. 

It took us three years to develop GitHub Copilot before we officially launched it to the general public. To go from idea to production, we followed three stages—find it, nail it, scale it—loosely based on the “Nail It, Then Scale It” framework for entrepreneurial product development.

Here’s how it breaks down:

  • Find it: Identify an impactful problem space for your LLM application
  • Nail it: Create a smooth AI product experience
  • Scale it: Get your LLM application ready and useable for general availability (GA)

Let’s get started.

Find it: Isolate the problem you want to solve

Sometimes the hardest part about creating a solution is scoping down a problem space. The problem should be focused enough to quickly deliver impact, but also big enough that the right solution will wow users. Additionally, you want to find a problem where the use of an LLM is the right solution (and isn’t integrated to just drive product engagement).

  • Get clear on who you want to help. We saw that AI could drive efficiency, so we wanted to prioritize helping developers who were consistently crunched for time, enabling them to write code faster with less context switching.
  • Focus on a single problem, first. Rather than trying to address all developer problems with AI, we focused on one part of the software development lifecycle: coding functions in the IDE. At the time, most AI coding assistants could only complete a single line of code.

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