Workflow · saas · workflow
How to build an enterprise LLM application: Lessons from GitHub Copilot
Developers were consistently crunched for time and needed to write code faster with less context switching, while most AI coding assistants at the time could only complete a single line of code.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Developer codes in IDE
Developers receive code suggestions without changing how they work.
Tools used
GitHub CopilotOpenAI APIMicrosoft AzureMicrosoft Experimentation PlatformAzure OpenAI ServiceGitHub Copilot Chat
Outcome
GitHub Copilot reached general availability with a neighboring tabs feature that increased suggestion acceptance rates by 5%, and the team scaled infrastructure on Microsoft Azure for enterprise-grade reliability.
What failed first
An early web interface required developers to switch back and forth between their editor and the browser, making it an unsuitable canvas. An initial assumption that every coding language would require its own fine-tuned AI model also proved incorrect as LLMs rapidly improved.
Results
Volume5%
Running since2021
Grounding & classification
Source type: technical build writeup
22 fields verified against source quotes.
code generationconversational aicode diff prfailure mode describedmetric backednamed customerproduction runtime claimedsource backedtools describedworkflow describedsoftwareaccuracy improvementemployee productivitytechnical build writeupai draft human approval