Automate declarative LM programming and self-improvement workflows with DSPy.
npx skills add https://github.com/anilcan-kara/nozich-agent --skill dspy-anilcan-kara
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/anilcan-kara/nozich-agent/tree/main/optional-skills/mlops/research/dspy Command: npx skills add https://github.com/anilcan-kara/nozich-agent --skill dspy-anilcan-kara
DSPy enables building self-improving language-model pipelines with a declarative approach, letting engineers compose prompts, reasoning steps, retrieval, and tools as modular components while iterating automatically.
Define a tiny DSPy module and run a basic QA task to observe the end-to-end flow.
High-intent search queries and answers about installing and using this skill.
You can build self-improving LM pipelines by defining modular signatures and using teleprompters to automatically synthesize better prompts from data. This declarative approach composes retrieval, reasoning steps, and tool usage for RAG and agent workflows.
Declarative LM programming modularly defines inputs, outputs, and signatures for complex tasks. Auto-optimization integrates teleprompters to synthesize improved prompts and instructions from your data, iteratively refining pipeline performance without manual prompt engineering.
You need a Python environment with DSPy installed to run declarative LM pipelines. The environment must support LM providers like OpenAI and Anthropic to execute the modular tasks and optimization teleprompters effectively.
Teleprompters optimize multi-stage reasoning and retrieval pipelines by automatically synthesizing better instructions from data. They support orchestrated reasoning, tool usage, and retrieval-augmented generation to gradually improve type-safe modules in production workflows.
Structure modular AI pipelines using type-safe signatures and reusable modules to compose reasoning steps and tools. Apply optimizers for gradual improvement, enabling production-ready workflows that handle complex tasks through automated prompt and instruction synthesis.
Manual prompt engineering fails for multi-stage LM tasks because it cannot scale to modular pipelines with retrieval and tool usage. Declarative programming with teleprompters solves this by automatically optimizing prompts and instructions from data.
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