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SkillsMP は lebsral/DSPy-Programming-not-prompting-LMs-skills から 95 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 95 件中 40 件を表示しています。
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…
原文の言語: 英語
Legacy reference for dspy.Assert and dspy.Suggest, both removed in DSPy 3.x - use dspy.Refine or dspy.BestOfN instead. Use when maintaining a legacy DSPy 2.x codebase that uses dspy.Assert or dspy.Suggest, or migrating from Assert/Suggest to DSPy 3.x. Also…
原文の言語: 英語
Runs any DSPy module N times and returns the highest-scoring output via a reward function using dspy.BestOfN. Use when output quality varies across runs and you want to sample multiple completions and pick the best — trading latency for reliability on…
原文の言語: 英語
Jointly optimize prompts and model weights using dspy.BetterTogether for maximum accuracy. Use when you have already tried prompt-only optimization and want the next level — jointly tuning prompts and model weights for maximum quality. Common scenarios - you…
原文の言語: 英語
Automatically generate and select few-shot demonstrations for a DSPy program from labeled data using dspy.BootstrapFewShot - the simplest and fastest optimizer. Use when you have 50+ labeled examples and want a quick accuracy boost as your first optimization…
原文の言語: 英語
Generates training data from a teacher model and fine-tunes student model weights using dspy.BootstrapFinetune. Use when you need maximum quality from a smaller/cheaper model — distilling GPT-4 quality into a cheaper model, generating training data from a…
原文の言語: 英語
Optimize few-shot demos for a DSPy program by searching over multiple candidate demo sets using dspy.BootstrapFewShotWithRandomSearch. Use when basic BootstrapFewShot is not enough and you want better results at the cost of more LM calls. Common scenarios -…
原文の言語: 英語
Use when the task benefits from intermediate reasoning before producing an answer — multi-step logic, analysis, math, or complex classification where direct prediction fails. Common scenarios - classification tasks where the model needs to reason about edge…
原文の言語: 英語
Deep dive into dspy.ChatAdapter -- the default adapter that formats DSPy signatures into multi-turn chat messages with field delimiters, parses LM responses back into typed Python objects, and falls back to JSONAdapter on failure. Use when you need to…
原文の言語: 英語
Build agents that write and execute Python code using dspy.CodeAct. Use when the task requires computation, data transformation, or multi-step logic better expressed as code than tool calls or natural language - data analysis, precise calculations, file…
原文の言語: 英語
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…
原文の言語: 英語
Prepare training and dev data for DSPy - loading from CSV/JSON/HuggingFace, creating dspy.Example objects, setting input keys, and building train/dev splits. Use when you need to prepare data for DSPy optimizers, load labeled examples, or convert dataset…
原文の言語: 英語
Combines multiple optimized DSPy programs into a single ensemble via majority voting, averaging, or custom reduce functions using dspy.Ensemble. Use when you have run multiple optimization passes and want to combine the best programs for more reliable…
原文の言語: 英語
Run dspy.Evaluate to score DSPy programs against labeled examples using custom or built-in metrics. 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…
原文の言語: 英語
Optimize instructions in DSPy programs via reflective evolution using dspy.GEPA without adding few-shot examples. Use when you want to optimize instructions without few-shot examples — a lightweight alternative when you do not have or do not want to use…
原文の言語: 英語
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…
原文の言語: 英語
Select the most relevant few-shot demonstrations for each input at runtime using dspy.KNNFewShot and dspy.KNN — embedding-based retrieval that adapts demos per query. Use when you want few-shot demos dynamically selected per input based on similarity, inputs…
原文の言語: 英語
Attach hand-curated labeled examples as few-shot demonstrations using dspy.LabeledFewShot — no bootstrapping, no metric, no LM calls at compile time. Use when you have hand-picked high-quality examples and want to inject them directly as prompt demos without…
原文の言語: 英語
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…
原文の言語: 英語
dspy.MIPROv2 jointly optimizes instructions and few-shot demos using Bayesian search - DSPy most powerful prompt optimizer. Use when you want the best possible accuracy from prompt optimization, jointly tuning instructions and few-shot demonstrations, using…
原文の言語: 英語
Build multi-step DSPy programs as composable, optimizable modules using dspy.Module and forward(). Use when composing Predict and ChainOfThought calls into a reusable pipeline, structuring a RAG pipeline as a class, creating nested DSPy modules, or designing…
原文の言語: 英語
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…
原文の言語: 英語
Use when you have independent LM calls that can run concurrently — batch processing, fan-out patterns, or speeding up pipelines with no data dependencies between steps. Common scenarios - processing a batch of inputs through a DSPy module concurrently,…
原文の言語: 英語
Build fast direct LM calls with dspy.Predict - the atomic DSPy module for simple input-to-output mapping with no reasoning overhead. Use when the mapping from input to output is straightforward and does not need reasoning steps — simple classification,…
原文の言語: 英語
DSPy typed wrappers (dspy.Image, dspy.Audio, dspy.Code, dspy.History, dspy.File, dspy.Reasoning, dspy.Tool, dspy.ToolCalls, dspy.Example, dspy.Prediction) for multimodal data, files, and structured outputs in signatures. Use when working with non-text inputs…
原文の言語: 英語
Use when the task requires precise computation, math, or data manipulation — the LM writes Python code that executes in a sandbox instead of reasoning in natural language. Common scenarios - math word problems, data manipulation tasks, precise calculations…
原文の言語: 英語
Build tool-using agents with dspy.ReAct - the Reasoning-Action-Observation loop for interleaved thinking and action. Use when the task requires calling external tools or APIs to gather information - multi-step tool use with reasoning, like searching…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
Recursive Language Model (dspy.RLM) that explores large contexts via a sandboxed Python REPL -- the LM writes code, queries sub-LMs, and iterates until it produces a final answer. Use when your input is too large for the context window, the model needs to…
原文の言語: 英語
Defines typed input/output contracts for LM calls using dspy.Signature, dspy.InputField, and dspy.OutputField. Use when you need to define the input/output contract for an LM call — choosing between inline and class-based signatures, adding type constraints,…
原文の言語: 英語
Optimize a DSPy program in small conservative steps using dspy.SIMBA (Stochastic Introspective Mini-Batch Ascent). Use when your program already works and you want to push accuracy higher without breaking what works, you need safe incremental improvement for…
原文の言語: 英語
Wraps Python functions as dspy.Tool objects and configures dspy.PythonInterpreter for sandboxed code execution in DSPy agents. Use when you need to give DSPy agents tool-calling abilities — wrapping Python functions as tools, building tool-using pipelines, or…
原文の言語: 英語
DSPy utility functions for caching control, debugging with inspect_history, saving/loading optimized programs, and runtime validation with Refine/BestOfN. Use when you need DSPy infrastructure - controlling the cache to avoid stale results, debugging with…
原文の言語: 英語
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,…
原文の言語: 英語
Use Langtrace for DSPy observability and tracing with langtrace.init() auto-instrumentation. Use when you want to set up Langtrace, langtrace-python-sdk, auto-instrument DSPy, trace DSPy calls, LLM observability, app.langtrace.ai, or self-hosted tracing. Also…
原文の言語: 英語
Use LangWatch for DSPy auto-tracing and real-time optimizer progress. Use when you want to set up LangWatch, langwatch.dspy.init, auto-tracing DSPy, real-time optimization dashboard, optimizer progress tracking, app.langwatch.ai, or DSPy optimizer dashboard.…
原文の言語: 英語
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…
原文の言語: 英語
Run DSPy with local models via dspy.LM and Ollama — no API key needed. Use when you want to run DSPy locally, use Ollama, set up a local LLM, run offline, or configure local model parameters. Also used for ollama, local model, run LLM locally, llama local,…
原文の言語: 英語
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…
原文の言語: 英語