Prompt Engineering
Crafting effective LLM inputs and structured reasoning — zero/few-shot, chain-of-thought, role and system prompts, ReAct, tree-of-thoughts, prompt chaining, and constitutional self-critique.
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Constitutional AI Prompting →
Constitutional AI prompting is a self-critique pattern where a model evaluates its own output against a set of defined …
Domain-Specific Prompting →
Domain-specific prompting is the practice of tailoring LLM instructions to match the vocabulary, constraints, and expert …
Multi-Turn Prompt Design →
Multi-turn prompt design is the practice of structuring conversation flows and managing context across multiple LLM …
Multimodal Prompting →
Multimodal prompting means writing instructions for AI models that can process images, audio, or video alongside text. …
Prompt Chaining →
Prompt chaining breaks complex tasks into sequential LLM calls where each step's output feeds into the next. Instead of …
Prompt Engineering →
Prompt engineering is the practice of designing inputs that reliably produce desired outputs from large language models. …
ReAct Prompting →
ReAct Prompting is a framework that structures LLM outputs as alternating Thought, Action, and Observation steps. Each …
Role Prompting →
Role prompting assigns an LLM a specific expert identity — such as senior security engineer or contract lawyer — before …
System Prompts →
A system prompt is a block of instructions placed before the first user message in an LLM conversation. It defines the …
Tree of Thoughts →
Tree of Thoughts (ToT) is a reasoning framework that extends chain-of-thought prompting by exploring multiple solution …