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#prompting (8)

Cheat sheets (8)

DSPy — Programmatic Prompting and Optimisation

Build LLM programs in DSPy with declarative signatures, modules, and optimisers. Covers Predict, ChainOfThought, ReAct, BootstrapFewShot, COPRO, MIPRO, MIPROv2, and inference compilation.
python dspy llm ai prompting optimisation stanford signatures modules

Prompting

Prompt engineering patterns, RAG, evaluations, few-shot, chain-of-thought, and structured output — foundational techniques for extracting reliable, structured behavior from LLMs.
prompting rag llm prompt-engineering cot few-shot evals structured-output

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.
chain-of-thought cot reasoning llm prompting self-consistency tree-of-thoughts thinking

LLM Evaluations

Build production evaluation pipelines for LLM applications — golden datasets, LLM-as-judge, rubrics, statistical significance, regression detection, and evals vs tests.
evals evaluation llm-as-judge golden-dataset regression statistics prompting

Few-Shot Prompting

In-context learning techniques — example selection, format design, count tuning, dynamic retrieval of demonstrations, and pitfalls of few-shot prompting.
few-shot in-context-learning icl prompting llm examples demonstrations

Prompt Engineering Patterns

Reliable prompt structures for reasoning, extraction, classification, generation, extended thinking, and vision tasks with Claude.
prompting llm ai claude chain-of-thought xml thinking

Structured Output

Techniques for reliable structured generation — JSON mode, schema-constrained decoding, function/tool calls as output, and validator pairing with Pydantic or Zod.
structured-output json schema pydantic zod tool-use function-calling prompting

AI

Claude Code, Codex CLI, the Claude API, and prompt engineering — practical reference for building with and using large language models.
ai llm claude prompting anthropic codex openai