lebsral

Use when you need to prepare training/dev data for DSPy optimizers — loading from CSV/JSON/HuggingFace, creating Examples, setting input keys, or building train/dev splits. Common scenarios - loading a CSV of labeled examples for optimization, converting HuggingFace datasets to DSPy format, creating train/dev/test splits, building Examples with proper input keys, converting JSON data for DSPy, or

Generate articles, reports, blog posts, or marketing copy with AI. Use when writing blog posts, creating product descriptions, generating newsletters, drafting reports, producing marketing copy, creating documentation, writing email campaigns, or any task where AI writes long-form content from a topic or brief. Powered by DSPy content generation pipelines., AI blog writer, generate marketing copy

DSPy retrieval modules (dspy.Retrieve, dspy.ColBERTv2, dspy.Embedder, dspy.retrievers.Embeddings) for searching documents, computing embeddings, and building RAG pipelines. Use when you need to search over documents, build a RAG pipeline, connect DSPy to a vector database, compute embeddings for semantic search, set up ChromaDB or Pinecone with DSPy, or build knowledge-grounded question answering.

Use MLflow for DSPy tracing, experiment tracking, and model registry. Use when you want to set up MLflow, mlflow.dspy.autolog, MLflow Tracing, MLflow experiment tracking, MLflow model registry, or full ML lifecycle management. Also used for mlflow setup, pip install mlflow, mlflow.set_experiment, mlflow UI, mlflow model versioning, mlflow OpenTelemetry, mlflow vs wandb, mlflow tracing DSPy, regist

Use Arize Phoenix for DSPy tracing and evaluation. Use when you want to set up Phoenix, arize-phoenix, openinference, DSPyInstrumentor, open-source trace viewer, localhost:6006, or LLM evals. Also used for phoenix setup, arize phoenix, pip install arize-phoenix, phoenix local UI, phoenix evaluations, DSPy trace viewer, open-source LLM observability, phoenix vs langtrace, openinference-instrumentat

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 Flask. Also use for deploy AI model to production, AI REST API, serve DSPy program over HTTP, Docker AI service, AI endpoint for mobile app, h

Generate smart notification messages from structured events using AI. Use when writing push notification copy, creating alert messages from events, building weekly digest emails, summarizing incidents from logs, personalizing notification text, generating Slack alerts from system events, smart email subject lines, aggregating multiple events into one digest message, notification copy for mobile ap

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 sem

Use when you need to configure which language model DSPy uses — setting up providers, API keys, model parameters, or assigning different models to different pipeline stages. Common scenarios - setting up OpenAI or Anthropic API keys, configuring model parameters like temperature and max_tokens, using different models for different pipeline stages, switching between providers, using local models wi

Find every way users can break your AI before they do. Use when you need to red-team your AI, test for jailbreaks, find prompt injection vulnerabilities, run adversarial testing, do a safety audit before launch, prove your AI is safe for compliance, stress-test guardrails, or verify your AI holds up against adversarial users. Covers automated attack generation, iterative red-teaming with DSPy, and

Iterative self-improvement with dspy.Refine -- wraps any module, scores each attempt with a reward function, generates feedback on failures, and retries until a quality threshold is met. Use when you want outputs to improve through self-critique, need iterative revision of drafts, or want the LM to learn from its own mistakes within a single request. Also used for self-critique and revise, iterati

Plan a multi-phase AI feature before building it. Use when you have a PRD or project idea and need to figure out the execution order, which skills to use in what sequence, or how to break an ambitious AI project into phases. Also use when you want to scope an AI feature, create a phased rollout plan, or figure out dependencies between AI components., help me figure out how to execute this, plan my