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Declarative programming for LM pipelines involves composing prompts and modules via signatures to build complex AI systems. You define what the pipeline should achieve using structured inputs and outputs, allowing the framework to handle prompt wiring and module orchestration automatically.

You optimize prompts automatically by applying built-in teleprompters like BootstrapFewShot, MIPRO, or COPRO. These optimizers adjust and improve your prompts across multiple LM providers, ensuring robust experimentation and effective performance without manual prompt engineering.

Yes, you can build RAG pipelines and agent orchestration using modular composition. By defining reusable modules with specific signatures, you can declaratively wire them together to create end-to-end workflows that integrate retrieval, generation, and tool use.

The best way to structure modules for end-to-end AI workflows is to define clear signatures for each step and compose them declaratively. This modular approach allows you to build reusable components for RAG pipelines or agent benchmarks while leveraging automatic prompt optimization.

Yes, understanding module signatures and available teleprompters is required to configure safe, effective prompt improvements. You need to know how to define structured inputs and outputs and select the appropriate optimizer to automatically enhance your LM pipelines.

Declarative LM programming replaces manual prompt engineering by using signatures and structured outputs to compose modules. This approach enables automatic prompt optimization through teleprompters, ensuring your pipelines self-improve and adapt across different LM providers without constant manual tuning.