The base model for the language you're using (which is trained on thousands of public GitHub repos).

IntelliCode generates its recommendations from multiple models by merging together: To build your team model, we extract a summary file with metadata on your types and their usages and securely upload it to our service. The more code you provide to illustrate your patterns of usage, the more capable your team model will be at offering useful recommendations. IntelliCode creates team models using the same learning process as for the IntelliCode base models, except they're trained on your own code. IntelliCode models are an encapsulation of a set of rules that let you predict some useful information (for example, recommendations in the IntelliSense list) based on an analysis of code. If you're writing C# and C++ code in Visual Studio, use IntelliCode to learn patterns from their code to make recommendations tailored to your code. If you're working on code that isn’t in that set of open-source repos, the base recommendations won't be useful to you.

Team completions are useful when working with your own types or domain-specific libraries that aren’t commonly used in open-source code, because IntelliCode’s base model recommendations are based solely on patterns learned from open-source GitHub repos.

Use IntelliCode team models for completions to get AI-assisted IntelliSense recommendations based on your C# and C++ code bases.

Visual studio reset intellisense cache