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Build declarative LM pipelines by defining structured signatures and modular blocks to compose multi-stage workflows. This approach provides telemetry to optimize prompts and orchestrate complex AI systems across multiple language model providers.

Optimize prompts for multi-stage AI workflows using data-driven optimization features within declarative LM pipelines. This structured approach allows you to systematically refine prompts through telemetry and modular block composition rather than manual adjustments.

Orchestrate RAG and agents across multiple LM providers by composing modular blocks within a declarative pipeline framework. This enables end-to-end system design with deployment-ready configurations while maintaining multi-provider language model support.

Yes, you can use modular blocks to compose end-to-end AI pipelines that are deployment-ready. By defining structured signatures and leveraging telemetry, you can orchestrate complex multi-stage workflows suitable for production environments across multiple LM providers.

Manual prompt engineering breaks down in complex AI pipelines because it lacks structured signatures and telemetry for systematic optimization. Declarative LM programming solves this by using modular blocks and data-driven optimizers to orchestrate reliable multi-stage workflows.