Generative AI Essentials: Prompt Engineering Masterclass

Acquire foundational knowledge in prompt engineering and learn how to craft effective prompts tailored to the task at hand in prompt engineering 101.

Craft strong prompts to improve AI output and save time by reducing back-and-forth. Prompt engineering is the core human–AI interface built from a few building blocks.

Explore how generative ai and llms produce text and media from prompts, trained on vast data, with prompts shaping outputs. Consider retrieval augmented generation and randomness in responses.

Master prompt basics by designing and testing prompts to elicit reliable, accurate outputs. Learn to craft clear instructions with context, role, goals, and iterative refinement to improve AI responses.

Explore conversational prompting and prompt engineering to craft efficient prompts, manage context and back-and-forth dialogue, and bundle insights into a single prompt for effective AI tasks.

Learn how tokens and context windows shape prompts in AI, including how tokenization works, why context windows matter, and practical tips for managing prompts and files.

Evaluate risk and complexity to tailor prompts for AI tasks, from low risk, low complexity to high risk, high complexity, as well-crafted prompts reduce errors and hallucinations.

Introduce the bridge framework and outline the six primary considerations for crafting prompts, explained through a very helpful acronym bridge.

Explore the bridge prompt engineering framework, including background, request, inputs, deliverable, guardrails, and evaluation, with actionable guidance on crafting prompts and avoiding hallucinations.

Master how to craft background context in prompts by defining your role, audience, and objective within the bridge framework. Keep prompts concise and use elevator pitch to steer AI outputs.

Define clear, specific requests so the AI acts precisely. Tailor prompts to a role and industry to generate organized interview questions, including technical, behavioral, and leadership topics.

Ground AI prompts with precise inputs from attached data, files, or URLs; describe sources, use one and multi-shot prompting, and avoid irrelevant material to prevent garbage in, garbage out.

Define precise deliverables to shape AI outputs, specifying CSV file deliverables, an executive summary under 300 words, and a forecasting table for monthly adspend revenue and profit margin.

Guardrails set boundaries to keep the AI on task, limit errors and avoid external benchmarks, while defining must-not-do behaviors to reduce hallucinations and maintain focus.

Learn how to evaluate AI outputs by prompting self-critique, listing guardrails and assumptions, and revealing data gaps to improve accuracy in high-stakes tasks.

Apply the bridge prompt template to craft reusable prompts by detailing background, inputs, deliverables, guardrails, and evaluation steps for any business task.

Explore the bridge checklist for effective prompting. Define background, requests, inputs, deliverables, guardrails, and evaluation to guide high-stakes AI work.

Build on a strong prompting framework by exploring best practices that focus on AI tool features to make prompting easier and more secure.

Explore data privacy when using AI, noting that shared information may be stored or used to train models; use temporary chats or enterprise options for sensitive data.

Explore how stored memories enable ChatGPT to recall context from past chats, personal preferences, and templates, and learn to manage, update, or forget those memories.

Explore prompt templates and memory-based reuse to share and refine prompts, then build a reusable data analysis template for the diamond price dataset using the bridge framework.

Discover how personalization settings tailor ChatGPT’s default responses, tone, and context with personalities, custom instructions, and stored memories. Learn to manage advanced features like web search, code, and privacy considerations.

Organize related chats and files with ChatGPT projects, preserving context across chats and creating a persistent knowledge bank for team collaboration on ETL and SQL tasks.

Select appropriate models to match task complexity and risk in prompt engineering, override automatic choices for high-stakes prompts, balance fast versus thinking models for brainstorming and strategic work.

Explore how custom GPTs create onboarding assistants with shared prompts and a knowledge bank, then tune tone and prompts for consistency and efficiency.

Learn how to create and customize a copy critic GPT that analyzes and rewrites copy in your company voice, using a style benchmark and knowledge bank to guide critique.

Explore team and enterprise plans that boost collaboration, integrate with tools like Google Drive and SharePoint, and enforce strong privacy controls to protect proprietary data.

Explore advanced prompting techniques to sharpen critical thinking about prompts, enhance brainstorming, and add context to make AI tools more effective.

Master meta prompting to have AI write prompts for you using the bridge framework to generate datasets and data dictionaries. Discover how guardrails guide concise, accurate prompts for structured outputs.

Learn to ask AI for questions to gather the right context, sharpen problem framing, and improve prompts; use a checklist on business context, data structure, and conversions for better analysis.

Learn chain-of-thought prompting to reveal how AI reasons, compare implicit and explicit thought processes, and guide metric prioritization with step-by-step transparency.

Use prompt branching to organize chats, explore multiple paths from one prompt, and compare outputs within separate conversations for focused, context rich brainstorming.

Master one-shot and few-shot prompting to control AI output, using a single example to fix format and multiple examples to teach patterns like writing style and custom classification.

Discover how multi-shot prompting trains AI with human-labeled examples to categorize employee feedback into key areas such as compensation and benefits, work-life balance, and process improvements.

Discover how XML tagging creates precise prompts and containers for outputs to automate reporting, dashboards, and workflows, and support retrieval augmented generation with key decisions, action items, and open questions.

Apply ai to analyze cafe sales and survey data, build customer personas, generate a sales insights report, and create 75-100 word marketing copy for the top five selling products.

Learn to apply a bridge framework to analyze three Maven roasters xls datasets, identify personas, link flavor notes to sales data, and craft marketing copy for the top five coffees.

Review the bridge framework for prompts, including background request inputs, deliverable guardrails, and evaluation. Use templates and memories, and apply chain of thought, branching, and multi-shot prompting for complex prompts.