IntelliCode team completions: AI-assisted IntelliSense based on your code

Use IntelliCode team models for completions to get AI-assisted IntelliSense recommendations based on your C# and C++ codebases. Team completions are useful when working with your own types or domain-specific libraries that aren’t commonly used in open source code. This is because IntelliCode’s base model recommendations are based solely on patterns learned from open-source GitHub repos. When working with code that isn’t in that set of repos, those recommendations aren't as useful to you. C# and C++ users in Visual Studio can now use IntelliCode to learn patterns from their code to make recommendations tailored to your code.

An IntelliCode model is an encapsulation of a set of rules that allow prediction of some useful information (for example, recommendations in the IntelliSense list) based on an analysis of code. IntelliCode creates team models using the same learning process as for the IntelliCode base models, except they are trained on your own code. The more code you provide to illustrate your patterns of usage, the more capable your team model will be at offering useful recommendations.

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.

How models get applied

IntelliCode generates its recommendations from multiple models by merging together:

  • The base model for the language you're using (which is trained on thousands of public GitHub repos)
  • Any team models you've trained
  • Any team models which are associated with the Git repository you’re working in

You don't need to manage which models apply to which solution or codebase because IntelliCode takes care of this for you.

Types of team completions models

There are two ways you can obtain team completions models:

  1. Repository-associated: Models are tied to the repository - all users who can clone and edit the repository are granted automatic access to the model. Your codebase must be under Git source control and pushed to a remote using our Azure Pipelines Task or our GitHub Action from your CI build to create a repository-associated model.

  2. Machine-associated: Models are available only on the machine they are trained upon.

Repository-associated team models

Repository-associated team models are available to users who train them using either Azure Pipelines or GitHub Actions.

  • Learn how to configure and automate your CI workflow (i.e. .yml file) to train Team Completions using a GitHub Action here.
  • Learn how to configure your Azure DevOps pipeline to train Team Completions here

Sharing your repository-associated models

Repository-associated models are automatically shared with others working in the same codebase as long as users have enabled automatic acquisition of team models in Visual Studio. Enable automatic acquisition by going to Tools > Options > IntelliCode > Acquire team models for completion.

When anyone clones and opens the codebase the model was trained on, any models associated with the configured Git remote repositories will be downloaded and activated. If you are working on a fork of the codebase, simply add the upstream codebase as a remote repository to get the model.

Access to the repository is access to the model. When training, we collect some information about the checked-out commit. Anyone who requests that model must have the same commit in their repository and be able to produce the same information that was collected during training in order to receive the team model.


The ability to share your user-associated team completions models via a sharing link, available in some preview releases of Team completions, is now deprecated

Delete your model

You can remove models from your account so they can no longer be used.

Delete a user-associated model created within Visual Studio

While your solution is open in Visual Studio, simply uncheck the checkbox accepting model training, on the View > Other Windows > IntelliCode page. The model will be deleted.

Delete a repository-attached model created using Azure DevOps or a GitHub CI workflow

Simply remove the training task from your pipeline - the associated model will be removed within 30 days if not trained.

Machine-associated models

Create and retrain your model

Train a machine-associated model:

  1. Open the project or solution in Visual Studio.
  2. Open the IntelliCode page by choosing View > Other Windows > IntelliCode
  3. Review and accept the license terms checkbox at the bottom of the page. A machine-associated model will be trained automatically.


You must repeat the above steps for each solution you want to train on.

Visual Studio will automatically retrain your machine-associated team completions models, periodically.

Train on a public codebase

Before you train on your own code, you might want to create a completions model on a public codebase. You can see how the completions model affects IntelliSense, or if you're concerned about the kind of data that IntelliSense collects, you can inspect the extracted data. Some interesting samples to train on are:

  • Azure ConferenceBuddy

    Fork the repo to your personal account, clone the repo, open the ConferenceBuddy.sln solution, build to check that it's working, and then train the model. You'll find some good completions on instances of the AskWhoTask class.

  • Windows RSS reader

    Fork the repo to your personal account, clone the repo, open the RssReader.sln solution, build to check that it's working, and then train the model. You'll find some good completions on instances of the MainViewModel class.

Data and privacy

To build your team model, we extract a summary file with metadata on your types and their usages. For example, the summary file contains the names of classes and methods and how often they're called in different circumstances. IntelliCode doesn't track your keystrokes or extract whole expressions, statements, or literal values (such as strings) from your code.

The extracted data is transmitted, over HTTPS, to the IntelliCode service. The service then uses machine learning algorithms to train a model for your code. It returns the model to your computer where it's merged with the base model.

View extracted data

To inspect the contents of the extracted data:

  1. Open the extracted data directory:

    • For repository-associated models: %temp%\Intellicode_Extraction_2019-10-23—234524
    • For machine-associated models: %TEMP%\Visual Studio IntelliCode
  2. To find and open the training for your most recent training session, sort the folder view by date (descending). The folder for your most recent training session is now at the top.


    There's one folder per training session in the %TEMP%\Visual Studio IntelliCode directory, each with a randomized name.

The folder contains the entire set of files that are sent to Microsoft when extraction is complete. The UsageOutput subfolder contains a JSON file that has the information IntelliCode extracts from your code to train the model. The UsageOutput_ErrorStats file contains any errors found when trying to build the extracted file and can help if Microsoft needs to debug issues.

IntelliCode model-training directory

If you want to inspect the extracted data for a different codebase before trying it on your own code, train a model on a public codebase.

How we secure your data

All data you send to and receive from the IntelliCode service is transmitted over HTTPS. You must sign in to Visual Studio in order to communicate with the service.

Models can only be retrieved:

  • machine-associated models: by the machine which submitted the extracted data for training
  • repo-associated models: by users who can prove they have access to the repository for repository-associated models.

This means that your model and what is learned about your code stays private to you and your intended collaborators.

If Microsoft needs to troubleshoot, authorized Microsoft service personnel may be granted access to your models and extracted data for diagnostic purposes only.

See also