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 contextual priming, a prompting technique that adds context and background information to guide a language model toward precise, task-specific outputs, with examples like professional emails and product briefs.

Learn knowledge injection: embed context and background in prompts to elicit accurate, context-aware responses for topics like light bending and product reviews.

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 constraint prompting, a prompt engineering technique that imposes explicit boundaries on format, length, content, and tone to tailor outputs for structured tasks or creative prompts.

Demonstrate delimiter prompting to clearly separate task, data, and context using characters like quotation marks, backticks, or brackets, reducing ambiguity in prompts and improving parsing by models.

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.

Introduce named prompting, assigning a reference name to prompt results for modular, reusable ai workflows. Chain prompts, preserve context, and use templates and placeholders for coherent multi-step tasks.

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 explicit questioning to craft direct, precise prompts that elicit answers. Clarify the task and format to minimize ambiguity, illustrated by asking for Japan's capital or a JavaScript factorial function.

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.

Master prompt chaining to break complex tasks into smaller prompts, feed outputs as inputs for a guided, coherent, and well-structured final result, with practical climate change and storytelling examples.

Master iterative refinement in prompt engineering by progressively refining prompts to improve model responses, clarify ambiguities, and guide outputs toward nuanced, creative results.

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.

Explore content filtering in prompt engineering to guide models toward safe, age-appropriate outputs by setting explicit boundaries, such as avoiding offensive language and violence, with kid-friendly prompts.

Master bias mitigation prompting techniques to reduce biases in AI responses by crafting clear, balanced prompts, avoiding stereotypes, and incorporating diverse perspectives with evidence-based, ethical considerations.

Explore how symbols shape prompt formatting, including quotation marks, parentheses, columns, asterisks, hyphens, angle brackets, square brackets, curly brackets, ellipses, and slashes to guide structure, emphasis, and clarity.

Explore techniques for formatting AI text outputs, show how to guide style with prompts, and demonstrate context-based formatting using italics, headings, and bold in an example.

Discover markdown formatting techniques for text output, including headers, italics, bold, lists, links, code blocks, and horizontal rules, with guidance on prompting for consistent cross‑model results.

Explore using HTML tag styling to control AI text output for web pages, employing H2 and H3 headings, italics, and prompts that render the page rather than show tags.

Control response length with word and paragraph limits, from 100 words to three paragraphs. Use elaboration or exact outputs, and enforce no prefix or suffix descriptions.

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.

Analyze the attached resume against the job description to determine qualification, outline gaps and strengths, provide actionable recommendations, and include a tabular and overall percentile rating.

Explore how trade routes shaped cultural exchange in the Silk Road era, detailing a timeline of events, traded goods, cultural influences, and trade-driven conflicts or alliances.

Explore 18 powerful image generation techniques to craft engaging prompts for stunning visuals, applicable to video as well, while comparing tools like Midjourney, Dall-E 3, Firefly, and Leonardo.

Learn to craft vivid prompts through descriptive narration that uses sensory detail and imagery to enhance scene description, mood, and perspective in text and image prompts.