lebsral

Use when you need to run DSPy modules asynchronously — FastAPI endpoints, concurrent LM calls, non-blocking execution, or integrating DSPy into async web frameworks. Common scenarios - serving DSPy behind FastAPI or Starlette, running multiple LM calls concurrently with asyncio.gather, non-blocking batch processing, combining async with streaming, or building async agent loops. Related - ai-servin

Use when you want to optimize instructions without few-shot examples — a lightweight alternative to COPRO when you do not have or do not want to use demonstrations. Common scenarios - optimizing instructions when you do not have or do not want to use few-shot demonstrations, lightweight instruction search as a first step, tasks where examples in the prompt confuse the model, or when you want fast

Use when you need to define the input/output contract for an LM call — choosing between inline and class-based signatures, adding type constraints, or using Pydantic models for structured outputs. Common scenarios - defining input and output fields for an LM call, adding type constraints to outputs, using Pydantic models for complex structured output, choosing between inline string signatures and

Fine-tune models on your data to maximize quality and cut costs. Use when prompt optimization hit a ceiling, you need domain specialization, you want cheaper models to match expensive ones, you heard fine-tuning will make us AI-native, you have 500+ training examples, or you need to train on proprietary data. Also use when you have spent weeks of manual iteration with no systematic improvement pat

Normalize and fix messy data fields using AI. Use when normalizing addresses, standardizing company names, fixing inconsistent date formats, cleaning CSV data before import, correcting typos in bulk data, normalizing phone number formats, standardizing job titles, cleaning up free-text fields, data quality improvement with AI, fixing formatting inconsistencies, bulk data normalization, preparing m

Use VizPy as a drop-in prompt optimizer for DSPy. Use when you want to try VizPy, vizops, ContraPromptOptimizer, PromptGradOptimizer, a commercial alternative to GEPA, a third-party prompt optimizer, or a different optimization backend. Also used for vizpy optimizer, vizpy vs GEPA, vizpy vs MIPROv2, commercial prompt optimization, ContraPrompt for classification, PromptGrad for generation, vizpy A

Strip PII and sensitive data from text before processing with AI. Use when redacting personal information, GDPR compliance, anonymizing customer data, masking credit cards, redacting PHI for HIPAA, stripping emails and phone numbers, de-identifying medical records, removing names from transcripts, PII detection and replacement, building a data anonymization pipeline, sanitizing text before sending

Use when you want higher accuracy by generating multiple reasoning chains and selecting the best answer — trading speed for quality on critical outputs. Common scenarios - high-stakes decisions where you want multiple reasoning paths compared, classification tasks where one chain of thought is not reliable enough, improving accuracy by generating several answers and selecting the best-reasoned one

Make your AI give the same answer every time. Use when AI gives different answers to the same question, outputs are unpredictable, responses vary between runs, you need deterministic AI behavior, or your AI is unreliable. Also used for same input gives different output every time, prompt sensitivity causes output changes with minor wording tweaks, reordering examples shifts accuracy dramatically,

LLM observability for DSPy with Langfuse -- auto-trace every LM call, attach scores and evaluations, run annotation queues for human review, and track experiments across prompt versions. Use when you want to set up Langfuse, langfuse.com, openinference-instrumentation-dspy, trace DSPy calls, LLM observability with scores, annotation queues, or experiment tracking. Also used for langfuse setup, pip

Switch AI providers or models without breaking things. Use when you want to switch from OpenAI to Anthropic, try a cheaper model, stop depending on one vendor, compare models side-by-side, a model update broke your outputs, you need vendor diversification, or you want to migrate to a local model. Also use when your prompt broke after a model update, prompts that work for GPT-4 do not work for Clau

Use when you want few-shot demos that are dynamically selected per input based on similarity — better than fixed demos when inputs vary widely. Common scenarios - inputs vary widely and fixed examples do not cover enough cases, dynamically selecting the most relevant demos per input, building a retrieval-augmented prompt with similar examples, or when static few-shot examples work for some inputs