Master the Art of Prompt Engineering for Generative AI

Discover how large language models power generative AI by optimizing prompts with a six-building-block framework: instruction, context, examples, persona, format, and tone, and practice applying it daily.

Clearly define the instruction as the core prompt building block, using action verbs and explicit scope to reduce guesswork, with examples like sentiment classification and blog-title generation.

Master just enough context in prompts by supplying essential background and environment to clarify intent, and use a layered approach—audience, tools, and time—to guide AI toward precise, relevant results.

Discover how to use few-shot prompting with examples to guide generative models, fill knowledge gaps, and shape task-specific outputs through input–output demonstrations.

Learn to harness persona prompts by assigning a fictional character to tailor responses, from a Google hiring manager to a funny teacher, a creative artist, or a student.

Define the desired output format for prompts in generative ai and apply bullet lists, step-by-step guides, tables, dialogues, q&a, and checklists to structure responses.

Set the right tone in prompts to guide response style, tailoring formal, casual, informative, persuasive, or creative outputs for audiences within AI and prompt engineering.

Discover how to combine instruction and context with optional blocks like examples, persona, and format and tone; iterate with feedback and use snooze the response to structure two-step reasoning.

Apply a simple framework for effective prompts, detailing instruction, context, examples, persona, format, and tone to guide generative AI responses.

Master the art of prompt engineering for generative AI by practicing a practical framework, exploring tools like ChatGPT and Google Gemini, and learning advanced methods for complex tasks.

Split complex tasks into smaller pieces to overcome ai input and output limits, feeding each piece to a dedicated prompt for clearer results in a customer support chatbot.

Leverage chain of thought prompting to reveal step-by-step reasoning, describe your reasoning step by step, and use predefined steps to improve model performance and transparency.

Explore chain of thought prompting and prompt chaining to create interactive, iterative prompts that refine ideas, brainstorm creatively, and develop content step by step.

Harness generated knowledge prompting to control a two-step workflow: first extract relevant knowledge from the model, then use that knowledge to perform the final task.

Break down complex tasks into simple prompts, and apply chain-of-thought prompting, interactive prompt chaining, knowledge generation, and external knowledge to handle domain-specific data.