#prompting
9 pages tagged prompting.
guidance
Package-level reference for the guidance library on PyPI — install, LLM-provider extras, versioning, and alternatives like instructor and outlines.
Structured Output
Techniques for reliable structured generation — JSON mode, schema-constrained decoding, function/tool calls as output, and validator pairing with Pydantic or Zod.
Prompting
Prompt engineering patterns, RAG, evaluations, few-shot, chain-of-thought, and structured output — foundational techniques for extracting reliable, structured behavior from LLMs.
Prompt Engineering Patterns
Reliable prompt structures for reasoning, extraction, classification, generation, extended thinking, and vision tasks with Claude.
LLM Evaluations
Build production evaluation pipelines for LLM applications — golden datasets, LLM-as-judge, rubrics, statistical significance, regression detection, and evals vs tests.
Few-Shot Prompting
In-context learning techniques — example selection, format design, count tuning, dynamic retrieval of demonstrations, and pitfalls of few-shot prompting.
DSPy
Build LLM programs in DSPy with declarative signatures, modules, and optimisers. Covers Predict, ChainOfThought, ReAct, BootstrapFewShot, COPRO, MIPRO, MIPROv2, and inference compilation.
Chain-of-Thought Prompting
CoT prompting techniques — zero-shot CoT, few-shot CoT, self-consistency, tree of thoughts, and how reasoning models compare with prompted reasoning.
AI
Claude Code, Codex CLI, the Claude API, and prompt engineering — practical reference for building with and using large language models.