Menu
SkillsMP a collecté 95 skills depuis lebsral/DSPy-Programming-not-prompting-LMs-skills. Ouvrez un skill pour examiner sa source et ses détails.
Affichage de 40 skills collectés sur 95.
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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 -…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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,…
Langue du texte source : anglais
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,…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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,…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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,…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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.…
Langue du texte source : anglais
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…
Langue du texte source : anglais
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,…
Langue du texte source : anglais
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…
Langue du texte source : anglais