Total 30,849 skills, AI & Machine Learning has 4972 skills
Showing 12 of 4972 skills
Break a failing complex AI task into reliable subtasks. Use when your AI works on simple inputs but fails on complex ones, extraction misses items in long documents, accuracy degrades as input grows, AI conflates multiple things at once, results are inconsistent across input types, you need to chunk long text for processing, or you want to split one unreliable AI step into multiple reliable ones.
Make your AI follow rules and policies. Use when your AI breaks format rules, violates content policies, ignores business constraints, outputs invalid JSON, exceeds length limits, includes forbidden content, or doesn't comply with your specifications. Covers DSPy Assert/Suggest for hard and soft rules, content policies, format enforcement, retry mechanics, and composing multiple constraints.
Fix broken AI features. Use when your AI is throwing errors, producing wrong outputs, crashing, returning garbage, not responding, or behaving unexpectedly. Covers DSPy debugging, error diagnosis, and troubleshooting.
Score, grade, or evaluate things using AI against a rubric. Use when grading essays, scoring code reviews, rating candidate responses, auditing support quality, evaluating compliance, building a quality rubric, running QA checks against criteria, assessing performance, rating content quality, or any task where you need numeric scores with justifications — not just categories.
Verify and validate AI output before it reaches users. Use when you need guardrails, output validation, safety checks, content filtering, fact-checking AI responses, catching hallucinations, preventing bad outputs, quality gates, or ensuring AI responses meet your standards before shipping them. Covers DSPy assertions, verification patterns, and generate-then-filter pipelines.
Chain multiple AI steps into one reliable pipeline. Use when your AI task is too complex for one prompt, you need to break AI logic into stages, combine classification then generation, do multi-step reasoning, build a compound AI system, orchestrate multiple models, or wire AI components together. Powered by DSPy multi-module pipelines.
Stop your AI from making things up. Use when your AI hallucinates, fabricates facts, isn't grounded in real data, doesn't cite sources, makes unsupported claims, or you need to verify AI responses against source material. Covers citation enforcement, faithfulness verification, grounding via retrieval, and confidence thresholds.
Scaffold a new AI feature powered by DSPy. Use when adding AI to your app, starting a new AI project, building an AI-powered feature, setting up a DSPy program from scratch, or bootstrapping an LLM-powered backend.
Condense long content into short summaries using AI. Use when summarizing meeting notes, condensing articles, creating executive briefs, extracting action items, generating TL;DRs, creating digests from long threads, summarizing customer conversations, or turning lengthy documents into bullet points. Powered by DSPy summarization.
Track which optimization experiment was best. Use when you've run multiple optimization passes, need to compare experiments, want to reproduce past results, need to pick the best prompt configuration, track experiment costs, manage optimization artifacts, decide which optimized program to deploy, or justify your choice to stakeholders. Covers experiment logging, comparison, and promotion to production.
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 isn't enough for the reasoning required. Covers ChainOfThought, ProgramOfThought, MultiChainComparison, and Self-Discovery reasoning patterns in DSPy.
Know when your AI breaks in production. Use when you need to monitor AI quality, track accuracy over time, detect model degradation, set up alerts for AI failures, log predictions, measure production quality, catch when a model provider changes behavior, build an AI monitoring dashboard, or prove your AI is still working for compliance. Covers DSPy evaluation for ongoing monitoring, prediction logging, drift detection, and alerting.