Generative AI has emerged as a game-changer, revolutionizing developer productivity and developer experience by offering unparalleled opportunities for innovation across sectors. These tools are redefining what's possible, accelerating workflows and enabling new forms of creativity. However the key to unlocking its potential at scale lies in the consistency of its deployment. This blog post is the first of a series, it covers tooling deployment at a conceptual level. The following posts will cover tooling deployment and usage in detail.
Generative AI development tools are renowned for their ability to not only produce highly diverse and creative outputs but to also help developers adapt to a new code base.
Examples include:
- Code auto-completion
- Help with Regular Expressions
- Writing documentation/commenting code
- Code base reference and navigation
- Generating and running test cases
- Assistance with code review
However, this strength can also introduce variability. Declarative deployments and regular updates ensure that the tools remain aligned with expectations. By maintaining a stable and consistent environment developers can better predict and depend on the AI's performance, making it a more valuable tool.
Trust is a critical component of any technology's adoption, and this is especially true for AI, where outputs can significantly influence decision-making, content creation and even personal or corporate reputations. Consistent deployments build user trust, as they demonstrate a commitment to quality, reliability, and security. Users are more likely to rely on and advocate for tools that meet their needs and adhere to ethical guidelines, including respect for copyright, privacy, and bias mitigation. By prioritizing consistent tooling deployment, developers can strengthen the trustworthiness of their code and encourage broader acceptance and use.
The obvious benefits of a Cloud Development Environment (CDE) revolve around developer velocity and code quality. Time to first line of code and time to first commit are drastically reduced by delivering the exact tooling and code base a developer needs in a pristine environment. Any new or updated tooling is instantly available to all the developers on the project. Code quality is increased since each developer’s build environment is the same as the next.
CDEs provide generative AI governance
While these benefits are easy to realize, a CDE deployed workspace is highly beneficial when developing in a regulated environment. As the use of Generative AI tools become more pervasive, it will face increasing regulatory scrutiny. Declarative deployment practices ensure that tools comply with corporate standards, avoiding potential misuse and mitigating risks related to privacy and security. As companies establish clear guidelines for the use of Generative AI tools, platform teams can use a CDE such as Coder to deploy generative AI governance to ensure the tool complies with and remains up-to-date on the specified guidelines.
Standardized tooling deployment is crucial for facilitating collaboration among teams and ensuring scalability. Standardized processes and environments allow for easier sharing of resources, models, and insights, fostering a collaborative culture that accelerates innovation. Moreover, consistent deployment practices ensure that AI tools can scale efficiently to meet growing demands without compromising performance or reliability.
Now for the fun part! Deploying generative AI at scale
Let’s take a look at how to easily enable standardized Generative AI deployments at scale with Coder. We’ll start with some of the most in-demand extensions:
- Deploy Generative AI at scale with one code block using Coder, VS Code Web and GitHub Copilot
- Deploy Generative AI at Scale with Gemini using Coder
Then we’ll take a look at using Coder to context switch between projects using different Generative AI extensions, seamlessly with zero reconfiguration:
- Coming soon: Multiple projects? Multiple workspaces! Avoid tooling conflicts with Coder
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