dspy-mlflow

More from lebsral/dspy-programming-not-prompting-lms-skills

• ai-do

  Describe your AI problem and get routed to the right skill with a ready-to-use prompt. Use when you are not sure which ai- skill to use, want help picking the right approach, or just want to describe what you need in plain language. Also use this when someone says I want to build an AI that..., how do I make my AI..., or describes any AI/LLM task without naming a specific skill, I need AI but do not know where to start, which AI pattern should I use, what is the best way to add AI to my app, recommend an AI approach, AI feature discovery, too many AI options, overwhelmed by AI frameworks, just tell me what to build, new to DSPy, beginner AI project help, which LLM pattern fits my use case, confused about AI architecture, help me figure out my AI approach.

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• ai-reasoning

  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 simple prompt is not enough for the reasoning required. Covers ChainOfThought, ProgramOfThought, MultiChainComparison, and Self-Discovery reasoning patterns in DSPy., AI gives shallow answers, LLM does not think before answering, chain of thought prompting, make AI show its work, AI fails at math, complex analysis with LLM, multi-step problem solving, AI reasoning errors, LLM logic mistakes, think step by step DSPy, AI cannot do basic arithmetic, deep reasoning with language models, self-consistency for better answers, tree of thought.

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• ai-building-chatbots

  Build a conversational AI assistant with memory and state. Use when you need a customer support chatbot, helpdesk bot, onboarding assistant, sales qualification bot, FAQ assistant, or any multi-turn conversational AI. Also used for chatbot remember previous messages, conversational AI keeps forgetting context, build a helpdesk bot that actually works, chatbot drops context after a few turns, Intercom bot alternative, Zendesk AI alternative, build WhatsApp bot, Slack bot with AI, chatbot escalation to human agent, LangChain chatbot but simpler, chatbot for SaaS onboarding flow.

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• ai-improving-accuracy

  Measure and improve how well your AI works. Use when AI gives wrong answers, accuracy is bad, responses are unreliable, you need to test AI quality, evaluate your AI, write metrics, benchmark performance, optimize prompts, improve results, or systematically make your AI better. Also used for spent hours tweaking prompts, trial and error prompt engineering is not working, quality plateaued early, stale prompts everywhere in your codebase, my AI is only 60% accurate, how to measure AI quality, AI evaluation framework, benchmark my LLM, prompt optimization not working, systematic way to improve AI, AI accuracy plateaued, DSPy optimizer tutorial, MIPROv2 optimization, how to go from 70% to 90% accuracy.

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• ai-parsing-data

  Pull structured data from messy text using AI. Use when parsing invoices, extracting fields from emails, scraping entities from articles, converting unstructured text to JSON, extracting contact info, parsing resumes, reading forms, pulling data from transcripts (VTT, LiveKit, Recall), extracting fields from Langfuse traces, or any task where messy text goes in and clean structured data comes out. Also use when emails are messy and lack structure, or structured data extraction from unstructured content is unreliable., extract entities from text, parse PDF with AI, structured extraction from unstructured text, OCR plus AI extraction, convert email to structured data, pull fields from documents automatically, AI data entry automation, invoice parsing, resume parsing with AI, medical record extraction.

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• ai-sorting

  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, topic classification, or any task where text goes in and a category comes out. Also use when classification accuracy varies between runs or semantically close categories get confused., auto-categorize support tickets, AI labeling system, text classification with LLM, auto-tag content, email routing with AI, intent classification, sentiment analysis with DSPy, spam detection with AI, topic modeling with LLM, build a classifier without training data, zero-shot classification, AI triage system.

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