lebsral

Use when you want to optimize instructions by generating many candidates and picking the best — useful when few-shot demos alone are not enough and you want to tune the task description itself. Common scenarios - your current task instructions produce mediocre results, you want to automatically generate and test many instruction variants, the task is hard to describe in one sentence, or few-shot e

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 n

Fix broken AI features. Use when your AI is throwing errors, producing wrong outputs, crashing, returning garbage, not responding, or behaving unexpectedly. Also use when you get Could not parse LLM output errors, DSPy program crashes, LLM timeout or rate limit errors, API key not working with DSPy, JSON parse error from LLM, model returns empty response, AI works sometimes but fails other times,

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 does not comply with your specifications. Also use when LLM JSON output is unreliable, you get inconsistent formatting with random spaces and line breaks, or there is extraneous text and co

Use Ragas to evaluate DSPy RAG pipelines with decomposed metrics. Use when you want to evaluate RAG quality, measure faithfulness, context precision, context recall, answer relevancy, or diagnose retriever vs generator issues. Also used for ragas, pip install ragas, ragas evaluate, RAG evaluation, faithfulness metric, context precision, context recall, answer relevancy, answer correctness, decompo

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.

Use when you want to extract interpretable decision logic from labeled examples — generating explicit rules that explain patterns in your data. Common scenarios - extracting business rules from labeled classification examples, understanding why a model makes certain predictions, generating human-readable decision criteria from data, building interpretable classifiers, or documenting implicit label

Build AI that takes actions, calls APIs, and does things autonomously. Use when you need AI to call APIs, use tools, perform calculations, search the web and act on results, interact with databases, or do multi-step tasks. Also AI that does things not just talks, tool-using AI agent, AI calls external APIs, function calling with DSPy, build AI that books appointments, AI workflow automation, agent

REMOVED IN DSPy 3.x -- use dspy.Refine or dspy.BestOfN instead (see /dspy-refine, /dspy-best-of-n). Legacy documentation for dspy.Assert and dspy.Suggest kept for existing codebases only. For new code, use dspy.Refine (iterative improvement with feedback) or dspy.BestOfN (sampling, pick best). Also used for dspy.Assert, dspy.Suggest, runtime validation for LLM output, retry on bad output, backtrac

Use when you need to measure how well your DSPy program performs — writing metrics, scoring against a dev set, or comparing before/after optimization. Common scenarios - measuring accuracy before and after optimization, writing custom metrics for your task, scoring a program against a held-out dev set, comparing two prompt strategies, building a test suite for AI quality, or running regression tes