Continue

What is Continue?

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Continue is an open-source AI coding assistant for VS Code and JetBrains. It helps developers work with customizable model providers, chat, autocomplete, edit workflows, and slash commands inside their IDE. (docs.continue.dev)

Understanding Continue

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In practice, Continue is designed to fit into a developer’s existing editor workflow instead of replacing it. The project provides IDE extensions plus a CLI, and the docs describe it as a platform for building AI-powered coding assistants with configurable chat, autocomplete, edit, and agent behavior. It also supports multiple model providers, including self-hosted setups, so teams can choose the backend that matches their privacy, cost, or performance needs. (docs.continue.dev)

Continue is also built around customization. Teams can define prompts, rules, model roles, context providers, and slash commands so the assistant behaves like a tailored coding tool rather than a generic chatbot. That makes it useful for organizations that want more control over how AI is used in day-to-day engineering work, from inline edits to repeatable commands for common tasks. (docs.continue.dev)

Key aspects of Continue include:

1. Open-source foundation: The core experience is open source, which makes it easier to inspect, extend, and adapt.
2. IDE integration: It runs inside VS Code and JetBrains, so developers can keep their normal editing flow.
3. Model flexibility: It supports multiple providers and can be configured for local or hosted models.
4. Slash commands: Custom commands let teams turn repeatable actions into keyboard-driven workflows.
5. Agent workflows: Continue supports broader AI coding tasks beyond simple chat, including multi-step edits and task execution.

Advantages of Continue

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1. Model choice: Teams can use the provider that fits their stack instead of being locked into one backend.
2. Developer familiarity: It works inside popular IDEs, which lowers adoption friction.
3. Workflow customization: Prompt files, rules, and slash commands let teams standardize useful patterns.
4. Local and self-hosted options: Privacy-conscious teams can route work through models they control.
5. Extensible by design: The CLI and configuration model make it easier to build team-specific AI tooling.

Challenges in Continue

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1. Setup complexity: More flexibility can mean more configuration work up front.
2. Provider tuning: Getting the best experience may require choosing and configuring the right model for each task.
3. Workflow design: Teams need to decide which prompts, rules, and commands are worth standardizing.
4. IDE-specific behavior: Results can vary depending on editor, model, and extension settings.
5. Governance needs: Larger teams may need clear policies for prompt usage, model access, and code handling.

Example of Continue in Action

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Scenario: a backend team wants an assistant that can explain unfamiliar code, draft tests, and generate commit messages without forcing everyone into the same model provider.

A developer opens a service file in VS Code, uses chat to ask for context, then runs a slash command to generate a shell command for a migration check. Another teammate in JetBrains uses the same shared prompt files to create a review checklist for API changes, while the team points Continue at a hosted provider for production work and a local model for sensitive repos.

That setup gives the team a consistent workflow across editors while still letting each project choose the right model and command set. It is a good fit when AI assistance should feel like part of the engineering system, not a separate tool.

How PromptLayer helps with Continue

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PromptLayer helps teams manage the prompts, commands, and agent-style workflows that tools like Continue rely on. If you are standardizing reusable prompt patterns or comparing model behavior across tasks, PromptLayer gives you a way to organize, version, and evaluate that work more systematically.

Ready to try it yourself? Sign up for PromptLayer and start managing your prompts in minutes.