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SkillsMP は lebsral/DSPy-Programming-not-prompting-LMs-skills から 95 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
lebsral/DSPy-Programming-not-prompting-LMs-skills収集済み skill 95 件中 15 件を表示しています。
Review DSPy code for correctness and best practices. Use when you want a code review of your DSPy program, need to check if your AI code follows best practices, want to find anti-patterns in your DSPy usage, or need a quality audit of your AI implementation.…
原文の言語: 英語
Pick the right DSPy module and architecture for your AI feature. Use when you are not sure whether to use Predict, ChainOfThought, ReAct, or a pipeline, need to choose between DSPy patterns, want architecture advice for your AI feature, or are deciding…
原文の言語: 英語
Fine-tune models on your data to maximize quality and cut costs. Use when prompt optimization hit a ceiling, you need domain specialization, you want cheaper models to match expensive ones, you heard fine-tuning will make us AI-native, you have 500+ training…
原文の言語: 英語
Make AI solve hard problems that need planning and multi-step thinking. Use when your AI fails on complex questions, needs to break down problems, requires multi-step logic, needs to plan before acting, gives wrong answers on math or analysis tasks, or when a…
原文の言語: 英語
Request or contribute a new AI skill that does not exist yet. Use when DSPy supports something but there is no skill for it — helps you build the skill and submit a PR, or file an issue requesting it. Also use when the user says there should be a skill for…
原文の言語: 英語
Build AI that searches your documents and answers questions. Use when building a knowledge base, help center Q&A, chatting with documents, answering questions from a database, search-and-answer over internal docs, customer support bot, or FAQ system. Also use…
原文の言語: 英語
Put your AI behind an API. Use when you need to serve AI features as web endpoints, add AI to an existing backend, deploy AI for other services to call, wrap a DSPy program in REST or HTTP, build an AI microservice, or put a language model behind FastAPI or…
原文の言語: 英語
Auto-sort, categorize, or label content using AI. Use when sorting tickets into categories, auto-tagging content, labeling emails, detecting sentiment, routing messages to the right team, triaging support requests, building a spam filter, intent detection,…
原文の言語: 英語
See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand…
原文の言語: 英語
Use when you need to customize how DSPy formats prompts for a specific provider — switching from chat to completion format, forcing JSON output, or debugging prompt rendering issues. Common scenarios - debugging why your prompt looks wrong when sent to the…
原文の言語: 英語
Use when you need to run DSPy modules asynchronously — FastAPI endpoints, concurrent LM calls, non-blocking execution, or integrating DSPy into async web frameworks. Common scenarios - serving DSPy behind FastAPI or Starlette, running multiple LM calls…
原文の言語: 英語
Use when you need structured source attribution in AI responses — verifiable citations that link claims to specific passages in source documents. Common scenarios - RAG with citation extraction, grounded answers with document references, legal or compliance…
原文の言語: 英語
Use when you need to connect DSPy agents to external MCP tool servers — databases, file systems, APIs, or any MCP-compatible service. Common scenarios - wiring MCP tools into a ReAct or CodeAct agent, discovering tools from an MCP server at runtime,…
原文の言語: 英語
Use when you need to stream LM output tokens to a frontend in real time — progressive responses, typing indicators, server-sent events, or WebSocket feeds. Common scenarios - streaming DSPy responses to a React UI, FastAPI SSE endpoint with DSPy, showing AI…
原文の言語: 英語
Use when working with reasoning models (o1, o3, o3-mini, DeepSeek-R1, Claude extended thinking) that reject system prompts or ignore formatting instructions. Common scenarios - using o1 or o3 with DSPy, getting structured output from reasoning models,…
原文の言語: 英語