Google explained how to develop Android applications with AI: six practical rules

Google has released a practical guide for developing Android applications with AI, and in doing so acknowledged a shift that has already occurred in the industry: ChatGPT, Claude, Gemini, and other assistants no longer serve merely as accelerators for individual tasks, but are becoming a full-fledged part of the mobile app development process. At the center of the recommendations is Gemini in Android Studio, but the logic itself is broader: if a developer works with code through a language model, simply writing a query and waiting for magic is no longer enough. Rules, context, and control over what exactly the agent does are needed.

Google's main thesis boils down to this: the quality of results is now determined not only by programming skills, but by how precisely a person can formulate a task for the model. The company recommends starting work not with an abstract command like "fix this code," but with a clearly defined context: what exactly needs to change, which constraints cannot be violated, which parts of the logic should not be touched, and where, conversely, one needs to check for architectural issues or security risks. Google specifically recommends explicitly forbidding the model from making up details when working with new or experimental APIs.

This is an important shift: AI-assisted development increasingly resembles not auto-generation, but managing a junior, but extremely fast executor.

The second block of recommendations concerns knowledge sources. Google acknowledges a typical problem of AI assistants: they confidently make mistakes when it comes to niche libraries, fresh SDKs, or rarely used APIs. Therefore, developers are advised to forcibly mix documentation into the agent's context. Android Studio has tools for searching and loading materials from Android Docs, and in more complex scenarios, you can connect external MCP servers with documentation. The idea is simple: the model should rely not on averaged memory, but on current primary sources. In practice, this reduces the risk of hallucinations and makes AI more useful precisely where its advice previously had to be double-checked manually.

Another important tip is to describe project rules in advance in special Agents.md files. They can store requirements for architecture, libraries used, design systems, code style constraints, and even lists of what the agent cannot do. Such files can be kept both in the project root and in individual modules to give the model local context. Essentially, Google proposes turning chaotic prompt work into a managed process where project knowledge is fixed next to the codebase. This is especially important for teams that have their own frameworks, non-standard UI components, or strict internal standards.

A separate emphasis is placed on accelerating routine work. Google proposes using Agent Mode and New Project Assistant not only for answering questions, but for actual work: generating application scaffolds, building architecture, creating interfaces in Jetpack Compose, writing tests, preparing commit messages, parsing JSON parsers, and even step-by-step verification of results through the Android Emulator. At the same time, the company emphasizes that the developer remains in the decision-making loop: the agent proposes a plan, shows changes, and gives the opportunity to accept or reject edits. For more complex tasks, you can connect a Gemini API key and expand the available context so that the model better retains memory of large projects and long instructions.

The sixth recommendation concerns privacy, and this is perhaps the most practical point for corporate development. Google reminds that sensitive parts of the project can be excluded from AI access through the .aiexclude file, which works on logic similar to .gitignore. In addition, context sharing can be limited at the project or user level. For companies this is critical: AI tools are becoming increasingly deeply embedded in the IDE, which means the question is no longer whether to use them, but how not to lose control over code, internal libraries, and workflows.

The main conclusion from these recommendations is simple: the era when AI in development was a toy for generating code snippets is ending. Google is effectively formalizing a new approach to Android development, where value shifts from mechanical writing of template code to the ability to design a task, give the model the right context, connect documentation, and establish interaction rules. Although Google speaks primarily about Gemini, these principles essentially become universal for any environment where code is written together with AI.

This does not cancel out engineering qualification, but changes its composition: the winners will not be those who type faster, but those who better manage the agent and verify results at the level of product, architecture, and security.