# Advanced

This section covers advanced topics and concepts for the Go AI SDK. Working with LLMs often requires a different mental model compared to traditional software development, and Go's concurrency primitives provide powerful patterns for building production-ready AI applications.

After reading these topics, you should have a better understanding of the paradigms behind the Go AI SDK and how to use them to build robust, scalable AI applications.

## Topics

- **[Prompt Engineering](https://goaisdk.com/docs/advanced/prompt-engineering.md)** - Learn advanced techniques for crafting effective prompts.

- **[Provider Architecture](https://goaisdk.com/docs/advanced/provider-architecture.md)** - Understand the provider abstraction layer: LanguageModel and ImageModel interfaces, ProviderOptions keys, implementing custom providers, middleware, and mock providers for testing.

- **[Backpressure](https://goaisdk.com/docs/advanced/backpressure.md)** - Learn how the SDK handles backpressure and cancellation with Go channels and contexts.

- **[Caching](https://goaisdk.com/docs/advanced/caching.md)** - Learn how to implement caching strategies to optimize performance and reduce costs.

- **[Rate Limiting](https://goaisdk.com/docs/advanced/rate-limiting.md)** - Learn how to implement rate limiting for production deployments.

- **[Model as Router](https://goaisdk.com/docs/advanced/model-as-router.md)** - Learn how to use a language model as an intelligent router to select the best tool or model for each request.

- **[Sequential Generations](https://goaisdk.com/docs/advanced/sequential-generations.md)** - Learn how to chain multiple AI generations together for complex workflows.

## Key Concepts

### Concurrency and Control Flow

Go's goroutines, channels, and contexts provide natural patterns for managing AI workloads:
- **Channels** provide automatic backpressure for streaming responses
- **Contexts** enable clean cancellation and timeout handling
- **Select statements** allow responsive concurrent operations

### Production Patterns

Building production AI applications requires:
- Robust error handling and retry logic
- Caching to reduce latency and costs
- Rate limiting to prevent quota exhaustion
- Monitoring and observability

## Next Steps

- Review [AI SDK Core](https://goaisdk.com/docs/ai-sdk-core.md) for API reference
- Learn about [Building Agents](https://goaisdk.com/docs/agents.md) for autonomous workflows
- Explore [Foundations](https://goaisdk.com/docs/foundations.md) for core concepts