dspy

Automated prompt optimization is achieved through declarative programming, which abstracts prompt engineering into modular pipelines. This approach allows the system to compile and refine prompts automatically, streamlining the AI development process without manual tuning.

Declarative programming for modular AI systems separates the logic of what a pipeline should accomplish from the specific prompt instructions. This creates maintainable, portable AI pipelines where components like RAG systems and agents are defined by their behavior.

Yes, you can build RAG systems and agents using dspy alongside OpenAI and Anthropic libraries. The framework supports modular AI system development, allowing you to create agents that perform sequential tasks with tools and RAG systems that retrieve and generate answers.

To get started with declarative AI pipelines, install the required dspy package and its associated libraries like openai and anthropic. This setup provides the foundation for building modular RAG systems, agents, and automated prompt optimization workflows.

Yes, automating prompt optimization requires the dspy library along with associated API libraries such as openai and anthropic. These dependencies provide the underlying model interfaces and framework components necessary to execute and compile declarative AI pipelines.

Declarative programming is preferred over manual prompt engineering when building complex, modular AI systems that require maintainability and portability. It automates optimization, making it ideal for developing scalable RAG systems and multi-step agents rather than single isolated prompts.