lebsral

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. Also used for LangChain LCEL alternative, how to chain LLM calls together, one prompt is not enough,

DSPy typed wrappers (dspy.Image, dspy.Audio, dspy.Code, dspy.History) for multimodal data in signatures. Use when working with non-text inputs like images, audio, or code, building multimodal AI pipelines, processing images alongside text, handling audio transcription inputs, working with code files as typed inputs, or managing conversation history in multi-turn chatbots. Also used for multimodal

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. Also use when building an LLM as a judge, a

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. Also used for AI summary too generic, summarize Slack threads, condense customer feedback, meeting

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 between a single module and a multi-step pipeline. Also use for which DSPy module should I use, Predict vs ChainOfThought, when to use ReAct, singl

Use when you need maximum quality from a smaller/cheaper model — generates training data from a teacher model and fine-tunes a student model weights. Common scenarios - distilling GPT-4 quality into a cheaper model, generating training data from a strong teacher to fine-tune a weak student, reducing inference costs by replacing an expensive model with a fine-tuned small one, or building a producti

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, Inter

Auto-moderate what users post on your platform. Use when you need content moderation, flag harmful comments, detect spam, filter hate speech, catch NSFW content, block harassment, moderate user-generated content, review community posts, filter marketplace listings, or route bad content to human reviewers. Also used for build content moderation system, UGC moderation at scale, user-generated conten

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 why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup., debug slow

Use Qdrant as a vector database with DSPy, or connect any vector DB (Pinecone, ChromaDB, Weaviate) with custom retrievers. Use when you want to set up Qdrant, QdrantRM, dspy-qdrant, vector database for DSPy, vector search, hybrid search, or build custom retrievers for Pinecone, ChromaDB, or Weaviate. Also used for qdrant, dspy-qdrant, QdrantRM, vector database, vector search, pinecone DSPy, chroma

Rewrite text to match a different tone, reading level, or audience using AI. Use when rewriting content in a different tone, simplifying legal language, adapting text for a different audience, converting technical docs to plain English, making formal text casual, adjusting reading level, matching brand voice in existing content, paraphrasing for clarity, converting jargon-heavy text to simple lang

Use when you have hand-picked high-quality examples and want to use them directly as few-shot demonstrations — no bootstrapping, just your curated demos. Common scenarios - you have expert-curated examples that you trust more than bootstrapped ones, hand-picked demonstrations for high-stakes tasks, using existing labeled data directly without bootstrapping, or when you want full control over which