Automate declarative language model programming and prompt optimization with dspy.
npx skills add https://github.com/LynxLabVN/office-agent --skill dspy-lynxlabvn
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/LynxLabVN/office-agent/tree/main/agent-core/optional-skills/mlops/research/dspy Command: npx skills add https://github.com/LynxLabVN/office-agent --skill dspy-lynxlabvn
This Skill solves the problem of manually engineering prompts for language models, offering a declarative approach to automate complex AI workflows.
Use the dspy skill to create a question answering system for your technical support chatbot.
High-intent search queries and answers about installing and using this skill.
You can automate prompt optimization by using declarative language model programming to define tasks modularly. This approach replaces manual prompt engineering by automatically improving model performance and reducing errors.
Declarative LM programming builds AI systems by defining task objectives directly instead of crafting manual prompts. It constructs modular AI pipelines for question answering, multi-hop reasoning, and agent-based systems.
Yes, building RAG and agent-based systems requires OpenAI and Anthropic dependencies to handle language model interactions. These libraries provide the foundational interactions for reasoning and question answering pipelines.
Yes, you can use declarative programming to create a question answering chatbot with multi-hop reasoning. You define the task in a few lines of code, optimize prompts, and integrate external tools like search engines.
The best way to build modular AI pipelines for multi-hop reasoning is using declarative LM programming. This method abstracts manual prompt engineering, allowing you to define tasks, optimize prompts, and construct agent-based systems efficiently.
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