Implement declarative language model programming with automatic prompt optimization.
npx skills add https://github.com/kngender5/hermes --skill dspy-kngender5
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/kngender5/hermes/tree/main/skills/mlops/research/dspy Command: npx skills add https://github.com/kngender5/hermes --skill dspy-kngender5
This Skill simplifies the development of AI systems by enabling declarative programming of language models, providing automatic prompt optimization, and modular AI pipelines.
Use the dspy skill to build a question-answering system using the following code:
import dspy
qa = dspy.ChainOfThought("question -> answer")
response = qa(question="What is the capital of France?")
print(response.answer)
High-intent search queries and answers about installing and using this skill.
Declarative language model programming allows you to build AI systems by specifying tasks naturally rather than crafting manual prompts, enabling automatic optimization and modular pipelines for complex workflows.
Automatic prompt optimization uses data-driven methods to tune language models for better performance, replacing manual prompt engineering with systematic adjustments based on evaluation data.
You can build a modular AI pipeline by combining RAG systems, classifiers, and text summarization components to create a customer support bot that provides accurate, efficient responses.
Yes, integrating with language models requires dependencies on the OpenAI and Anthropic libraries, which provide the underlying model calls for executing your declarative AI workflows.
Yes, declarative programming supports modular AI components, allowing you to combine classifiers, text summarization, and other modules to build complex, multi-component reasoning systems.
Declarative language model programming requires managing dependencies like dspy, openai, and anthropic, and involves complex multi-component workflows and data-driven optimization that demand advanced implementation knowledge.
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