Build Production AI Workflows with TypeScript and Temporal
Learn to build production AI workflows using TypeScript and Temporal. Master prompt versioning, LLM-as-judge evaluators, and secure, scalable orchestration.
Bestes Zitat aus Build Production AI Workflows with TypeScript and Temporal
The number one mistake that causes systems to fail in production is mixing non-deterministic I/O, like LLM calls, directly into your main logic. You must treat orchestration like a distributed systems problem, keeping your pure orchestration logic in Workflows while pushing all messy I/O into a dedicated Step layer.
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Build a TypeScript framework for production AI workflows. Separate Workflows (pure orchestration) from Steps (the I/O layer — LLM calls, HTTP, DB queries — each result cached for replay). Store prompts as version-controlled .prompt files with YAML and Liquid templates; swap providers in one line. Add LLM-as-judge Evaluators for quality scoring. Scale via Temporal for retries, replay, and parallel execution. Encrypt secrets with AES-256-GCM. Stack: TypeScript, Temporal, Vercel AI SDK, Zod.
Häufig gestellte Fragen
This framework utilizes Temporal to manage production AI orchestration, ensuring workflows are durable, scalable, and reliable. By separating pure orchestration logic from I/O-heavy steps like LLM calls or database queries, the system can leverage Temporal’s replay and retry capabilities. This architecture allows developers to execute complex parallel processes while maintaining a clear state, making it ideal for high-stakes production environments where consistency is critical.
LLM-as-judge Evaluators provide a sophisticated layer for quality scoring within your AI workflows. By using specialized LLM calls to evaluate the output of other models, you can automate testing and ensure high-quality results at scale. This approach integrates seamlessly with the Vercel AI SDK and Zod validation, allowing you to define strict schemas and scoring rubrics that maintain the integrity of your production AI applications.
Prompts are managed as version-controlled .prompt files using YAML and Liquid templates, allowing you to swap providers with a single line of code without redeploying logic. For security, sensitive data and API keys are protected using AES-256-GCM encryption. This combination ensures that your AI prompts remain flexible and organized while your production secrets stay encrypted and secure against unauthorized access.
Separating Workflows from Steps creates a clean boundary between orchestration logic and the I/O layer. Steps handle specific tasks like HTTP requests or DB queries, with each result cached for replayability. This design ensures that the core workflow remains deterministic and pure, which is essential for Temporal’s replay functionality. It also simplifies debugging and testing, as individual steps can be mocked or updated without affecting the overall orchestration flow.
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