Sourcegraph Cody works across your entire repository and that context difference is what makes it useful at scale

I work on a large codebase with a long history. Most AI coding assistants get noticeably less useful as the codebase grows because they work from a limited context window and have no real understanding of how the different parts of a system connect. Sourcegraph Cody is built differently and that difference is worth explaining properly.

The full codebase context is the core design principle. It uses your entire repository rather than just the current file or a few manually tagged references to inform its suggestions. When you ask it to explain why a particular function behaves the way it does, or to generate something that needs to integrate with existing patterns, it has the actual context rather than making assumptions. For large or complex codebases that translates to meaningfully better and more relevant suggestions.

The AI Chat and Commands cover explaining complex code sections, generating unit tests, and identifying code smells across the codebase. The /explain command for understanding unfamiliar code is the one I use most often when onboarding into a new area of a large project.

Autocomplete and inline editing via natural language prompts work the way you would expect from a modern AI coding assistant. The model selection between Claude 3.7 Sonnet and GPT-4o gives you flexibility based on the task and your preferences.

The enterprise security options, self-hosted deployment and single-tenant cloud, are what make it viable for organizations where data residency and security requirements rule out standard cloud-based tools. That is a real distinction from most AI coding assistants that are cloud-only with no self-hosting option.