How to Use an AI Coding Assistant in 2026
A practical guide to getting started with AI coding assistants. Learn best practices, common pitfalls, and how to maximize productivity with terminal-based AI tools.
Getting Started with AI Coding Assistants
AI coding assistants have evolved from simple autocomplete tools into sophisticated development partners. In 2026, terminal-based AI assistants like Aurict offer multi-agent orchestration, contextual skills, and deep codebase understanding.
Choosing the Right Tool
When selecting an AI coding assistant, consider these factors:
- Provider flexibility — Can you switch between Anthropic, OpenAI, Google, and others?
- Agent architecture — Does it use specialist agents or a single model?
- Context awareness — Does it understand your codebase automatically?
- Platform support — Does it work on macOS, Linux, and Windows natively?
Installation and Setup
Installing Aurict takes less than a minute:
The first-run flow guides you through provider selection, API key configuration, model choice, and the optional Project Auto decision for bounded file changes in the current project session.
Best Practices
To get the most out of your AI coding assistant:
- Be specific in your requests — vague prompts get vague results
- Use the right agent for the task — explore, code, review, test, etc.
- Leverage persistent memory — the AI remembers your preferences
- Review AI suggestions — trust but verify
Conclusion
AI coding assistants are no longer experimental — they're essential tools for modern development. Aurict's multi-agent approach and contextual skills make it a powerful choice for developers who want terminal-native workflows.