Compile declarative language model calls and optimize prompts with DSPy.
npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill dspy-zhouboyu-xreal
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/zhouboyu-xreal/Hermes-Memory/tree/main/skills/mlops/research/dspy Command: npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill dspy-zhouboyu-xreal
This Skill helps automate prompt optimization and improve language model performance with declarative programming.
Use the dspy skill to create a QA module for the language model.
High-intent search queries and answers about installing and using this skill.
Automatic prompt optimization uses declarative programming to define signatures and modules, leveraging data-driven methods to refine prompts without manual engineering. This approach compiles language model calls and optimizes them using trained data.
Declarative programming for AI systems builds multi-component workflows by defining signatures and modules rather than writing manual prompts. This mechanism optimizes language models by compiling calls and applying data-driven techniques automatically.
Yes, the DSPy library is required to compile declarative language model calls and optimize them using trained data. It provides the foundational framework for creating signatures and modules without manual prompt engineering.
Yes, declarative programming for prompt optimization supports OpenAI and Anthropic models as dependencies. You can build and optimize multi-component AI systems using these providers within the framework.
The best way to build a complex multi-component AI system without manual prompt engineering is using declarative programming. Define signatures and modules to construct workflows, then apply automatic prompt optimization using data-driven methods.
Automatic prompt optimization requires trained data to apply data-driven methods that refine prompts accurately. This data enables the system to evaluate and optimize language model performance across multi-component workflows effectively.
bailynloveConstruct declarative language model programming pipelines for AI tasks.
8b-isBuild type-safe DSPy.rb workflows with signatures, modules, and provider configurations.
sourcegraphGenerate aggregate CodeScaleBench evaluation reports from Harbor runs in markdown, JSON, and CSV.
SignmanalDefine type-safe LM task signatures and automatically optimize prompts with teleprompters.
