Operations Guide
AI SDK Best Practices Guide (2026) - Application Development
AI SDK best practices: use official SDKs (latest version), configure timeouts/retries, handle errors properly, implement streaming for long outputs, optimize for performance.
Direct answer
AI SDK best practices: use official SDKs (latest version), configure timeouts/retries, handle errors properly, implement streaming for long outputs, optimize for performance.
Fast path
- Official SDKs: use provider SDKs (Anthropic SDK, OpenAI SDK), update regularly.
- Configuration: set timeouts (5-30 seconds), retry limits (3), connection pool size.
- Error handling: catch specific exceptions, map errors to application logic.
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Implementation Steps
- Official SDKs: use provider SDKs (Anthropic SDK, OpenAI SDK), update regularly.
- Configuration: set timeouts (5-30 seconds), retry limits (3), connection pool size.
- Error handling: catch specific exceptions, map errors to application logic.
- Streaming: use streaming for long outputs, reduce perceived latency.
- Performance: async calls, connection reuse, batch where possible.
Frequently Asked Questions
Which AI SDKs to use?
Use official AI SDKs: Anthropic SDK (for Claude), OpenAI SDK (for GPT), Google AI SDK (for Gemini), AWS Bedrock SDK (for multiple models). Official SDKs handle retries, streaming, auth correctly. Keep updated for new features.
How to configure AI SDK timeouts?
Configure AI SDK timeouts: connect timeout (5-10 seconds), request timeout (5-30 seconds based on task), streaming chunk timeout (1-2 seconds). Adjust based on expected response time. Too short = unnecessary failures, too long = hung requests.
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