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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 - Learn advanced techniques for crafting effective prompts.

  • Provider Architecture - Understand the provider abstraction layer: LanguageModel and ImageModel interfaces, ProviderOptions keys, implementing custom providers, middleware, and mock providers for testing.

  • Backpressure - Learn how the SDK handles backpressure and cancellation with Go channels and contexts.

  • Caching - Learn how to implement caching strategies to optimize performance and reduce costs.

  • Rate Limiting - Learn how to implement rate limiting for production deployments.

  • Model as Router - Learn how to use a language model as an intelligent router to select the best tool or model for each request.

  • Sequential Generations - 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​