42 AI Prompt Engineering Techniques - Text & Images Prompts

Learn zero-shot prompting, instructing a language model to perform tasks without examples, relying on pre-trained knowledge, with clear, concise instructions and constraint-driven outputs.

Explore structured output prompting techniques by explicitly defining formats like json, xml, tables, or bullets to produce machine-readable, consistent outputs for application programming interfaces, databases, and reports.

Learn instruction prompting in prompt engineering, a technique that guides a language model with clear, detailed directives to produce precise, structured responses for multi-step tasks.

Master contextual labeling to guide language models with labels and section markers, reducing ambiguity and improving output accuracy. Use idiom, code, and article labels to organize prompts and formats.

Explore chain of thought prompt engineering techniques to guide models through a structured, step-by-step reasoning process, revealing intermediate steps and transparent reasoning for complex problems.

Master multi-turn dialogue prompting, a back-and-forth between user and language model that builds on prior responses and retains context to provide step-by-step guidance and deeper topic exploration.

Explore reverse prompting, a prompt engineering technique that lets the model ask clarifying questions to guide task framing. Use it to improve input quality and collaborative problem solving with ai.

Define a role to guide the AI's responses via row prompting, tailoring expertise and tone. Use examples like a creative writing coach or customer service agent to produce focused outputs.

Combine zero shot, few shot, and chain of thought prompting to gain control over language model outputs, and apply a step-by-step, structured outline for climate change impacts on marine biodiversity.