Prompt Engineering and AI Literacy Masterclass

Discover how generative AI uses large language models to generate text and analyze images, then craft context-rich prompts to tailor outputs such as emails and recommendations.

Turn off your Google brain and treat AI prompts as a thought partnership, engaging in back-and-forth dialogue with the AI as a virtual expert to get more tailored results.

Plan a real-time Italy itinerary with ChatGPT as a thought partner, iterating three differently styled itineraries for a family of four, using detailed context.

Explore workplace use cases for ChatGPT, from crafting marketing campaigns and presentation prep to fitness planning and career coaching, using thought partnership with an LLM for tailored insights.

Apply module learnings by crafting a detailed travel itinerary with a language model, iterating through prompt, refine, and finalize steps for a practical, hypothetical or real trip.

Learn a two-step prompting formula: state your objective and why. Apply the what and why approach to a Chicago trip example to create itineraries and dining suggestions.

think first, then prompt to practice deliberate thought partnership with llms, using priming to surface unknown unknowns like food safety and allergen cross-contamination during tasks such as meal prepping.

Master think-first prompting to improve AI outputs: learn to craft specific, context-rich prompts, prime the model, and produce more human, task-focused emails and prompts.

Master prompting with the two w's—the what and the why—to produce relevant, on-topic outputs. Keep prompts conversational, avoid code-toned prompts, and frame goals, context, and guardrails as a trusted advisor.

Learn to show don't tell by feeding ai rich context, priming it with attachments and text, and crafting two context-driven, protein-rich salads from available ingredients.

Boost your prompt engineering and AI literacy by learning how to supply rich context to an llm with attachments such as photos or pdfs, plus real-world thread examples.

Explore how external context acts as brain food for LLMs in prompt engineering and ai literacy by selecting engaging material you’d enjoy in conversation and applying it to a task.

Learn to add context to an LLM conversation and explain what and why for better outputs, using photos and file types as examples.

Learners partner with an llm to achieve a specific end goal by using external context and an initial prompt, then refine to produce a final actionable meal plan.

Explore how golden rules 1–3 improve prompting outcomes through thought partnership, what-and-why planning, and show-don’t-tell context. Use a reflection activity to overcome subpar responses and strengthen future prompts.

Push through subpar responses under golden rule four by using course correction and treating the model as a thought partner, refining ideas with back-and-forth dialogue and the maps pin analogy.

Apply golden rule four to push through subpar responses in prompt engineering, using a hands-on activity to refine broad prompts into precise, actionable outputs.

Learn in-thread meta prompting, golden rule five, by asking the AI how to prompt it within the same thread; practice refining prompts, crafting tailored value propositions, and running A/B tests.

Practice cross-thread metaprompting to refresh memory and gain new insights by starting a fresh thread, copying partial text, and using rewind reboot to critique prior outputs for better prompts.

Push the limits with golden rule number six, embracing experimentation and failure to unlock infinite tools for prompt engineering and creative uses of large language model outputs.

Review the golden rules and complete course correction, in-thread and cross-thread meta prompting to explore new use cases for LLMs and prepare for module four with ChatGPT, Claude, and Gemini.

Master upfront meta prompting and piggyback prompting to guide prompt creation from the start. Compare outputs across multiple LLMs to refine prompts for tasks like competitive marketing analysis.

Explore cross validation and cross thread meta prompting to have another LLM judge your chat, evaluate outputs, detect hallucinations, and improve educational content with structured chapters and quizzes.

Practice collaborative AI by using an LMS as editor and creator in a back-and-forth prompt loop between ChatGPT and Claude, including piggyback prompting and evaluation, to refine outputs.

Explore four module four assignments: piggyback prompting, upfront meta prompting, their combination, cross Elm validation, and voice mode practice for interviews and negotiations.