ChatGPT Prompt Engineering: AI Masterclass From Zero to Hero

Explore prompt engineering fundamentals, definitions and basics, including categories of prompts, components of a well-structured prompt, and how precise instructions improve AI responses through hands-on demos.

Master prompt clarity by avoiding vague prompts and overcomplex requests, and by adding format guidance; tweaks boost AI accuracy and reduce edits, as shown with climate change’s impact on agriculture.

Master prompt clarity by breaking down complex prompts into specific tasks, adding format guidance, using examples like a futuristic AI novel to elicit structured ChatGPT responses.

Explore four prompt categories—descriptive, instruction-based, conversational, and role-based—and see how they shape AI responses, using blockchain examples to explain definitions, uses, engagement, and investment risk.

Design clear, structured prompts and refine them to improve AI responses. Choose a topic, write a prompt, then enhance it with the three-step triangle prompt model and test in ChatGPT.

Explore the three-step triangle model to craft clear, task-focused prompts, adopting a certified fitness coach persona to structure responses with 200 words and three key benefits.

Master the core building blocks of an effective prompt, including delivering the right amount of information, respecting token limits, and using real-world analogies like food ordering and memory nodes.

Emphasize that context drives prompt relevance, showing how background information yields specific, accurate results and how precise prompts—like a four-line poem request—avoid vague responses.

Analyze how tokens power GPT models by counting prompts and responses as tokens. Compare token limits across GPT-3, GPT-4, and GPT-4 turbo using the notebook-page analogy.

Explore tokenization and embedding lookup to show how lion, cat, tiger, and plane form contextual vectors. Observe semantic clustering and two-dimensional embeddings guiding next-word prediction in large language models.

Explore how prompt length affects clarity and avoids cognitive overload, and learn to craft well-structured prompts with defined roles, audience, tone, and SEO.

Rewrite five poorly written prompts with added context, adjusted length, and token awareness, then explain why each version improves and share rewrites in comments, summarizing the assignment wrap-up.

Explore how to rewrite prompts for clarity, context, and structure, using concrete examples from history such as World War II, beginner AI explanations, data science book lists, and translation tasks.

Explore structured prompting and role-based prompts, with ai personas to tailor responses across domains, and balance direct versus open-ended prompts, creativity, and tone, including when to guide format.

Learn how direct prompts differ from open-ended prompts, using task-oriented, concise examples and a guest-speaker analogy to explore productivity and brainstorming prompts.

Learn persona prompting by assigning AI roles—doctor, cardiologist, fitness influencer—to tailor heart health explanations for different audiences, using simple analogies. Compare role-based prompts to generic ones to see output differences.

Master controlling ai creativity with temperature and adopt formal tone for professional outputs. Craft prompts for casual blogs and formal reports, from eco friendly topics to government summaries.

Explore image and video generation through prompts with ChatGPT and other LLMs, using tools like Midjourney and InVideo to craft future city visuals and AI ethics scenes.

Craft and compare three prompts—direct, persona-based, and open-ended—on topics like cyber security, nutrition, travel, or AI ethics, then learn how prompt structure, tone, and personas shape AI responses.

Explore task-specific prompts with a format and a list of foods to keep nutrition outputs factual. See persona-based prompts define roles and audiences, and target muscle recovery for college athletes.

Tailor prompts to different LLMs and Genei LMS platforms to achieve consistent results with ChatGPT, Gemini, palm, Kahari Lama, and perplexity AI. Learn API prompt structuring and a Python intro.

Compare GPT-4’s balanced reasoning and creativity for education, writing, and logic. Learn how Gemini, Palm, Lama, Cohere, and Perplexity AI offer multi-modal, instruction-tuned, and real-time web data capabilities.

Explore practical demos of few large language models, including Claude, Gemini, and perplexity, with style controls and automatic model selection for tailored prompts and explanations.

Learn to call the ChatGPT api from Python using variables, functions, and the OpenAI library with an api key, structure messages with user, system, and assistant roles, and print results.

Explore the ChatGPT developer platform, access API docs, model versions, and pricing, and use the hands-on playground to craft prompts, system messages, and code-backed responses.

Apply prompt engineering at the API level to gain control, repeatability, and scalability for apps. Demonstrate testing and executing prompts in Python with Colab and modular functions.

Compare ai platforms by running a single prompt to observe differing responses across ChatGPT, Claudii, perplexity, and Gemini; use python with the ChatGPT API to replicate prompts.

Explain quantum computing to a non-techie using three platforms—chatgpt gpt-4, anthropic claude, and perplexity ii—focusing on qubits, superposition, and entanglement, with beginner-friendly analogies and real-world uses like encryption.

Learn advanced prompt engineering techniques, including chain of thought and few-shot prompting, and distinguish zero-shot prompting from few-shot prompting to boost AI reasoning and task accuracy.

Explore zero-shot prompting with large language models like GPT, ChatGPT, Gemini, and Claude, showing fast but not always accurate results from a single-command setup.

Learn few-shot prompting to boost model performance through in-context learning by supplying demonstrations in prompts, and practice sentiment classification and multilingual prompts with practical debugging tips.

Explore chain-of-thought prompting and few shot prompting to improve reasoning by guiding step-by-step explanations, demonstrated with math examples and improved model accuracy.

Explore chain-of-thought prompting with a hands-on demo of prompting a ChatGPT API, including detective-mystery reasoning, sentiment analysis, and step-by-step sorting prompts. See how prompts guide reasoning in a structured chain.

Explore ReAct prompting by toggling reasoning and action, integrating chain-of-thought with external tools like web searches or APIs to improve factual responses.

Explore a ReAct demo that shows how to build a system message guiding a model through thought, action, and observation steps, with few-shot prompting and a final answer.

Explore self-consistency and chain-of-thought prompting to improve arithmetic and common-sense reasoning, and learn tree of thoughts, multi-modal prompting, and prompt chaining to break tasks into subtasks and synthesize single output.

Create four prompts using zero shot prompting, few shot prompting, chain of thought prompting, and react style prompting. Explore topics like remote work and explain pattern learning and step-by-step reasoning.

Learn to use zero-shot, few-shot, chain-of-thought, and react-style prompts to craft structured prompts, analyze responses, and guide step-by-step reasoning for entrepreneurship topics.

Diagnose why prompts fail and fix them by applying the most suitable prompting technique for each problem, while refining clarity, consistency, and control to turn vague outputs into precise responses.

Diagnose and fix prompt failures by addressing generic replies, tone inconsistencies, and reasoning errors. Use few-shot prompting, chain-of-thought prompting, role-based prompting, react prompting, and instruction tuning, with a guitar-tuning analogy.

Debug prompts by identifying vague structure and tone, and apply few-shot prompting, chain-of-thought prompting, role-based prompting, react prompting, and instruction tuning to improve consistency and accuracy.

Demonstrate building a LangChain prompt template using Jinja2, with adjective and topic as dynamic variables, to generate a formatted prompt like 'give me an interesting fact about space exploration.'

Explore a visual prompt triage map to diagnose AI responses, using few-shot prompts, chain-of-thought prompts, persona-based tone, and format constraints to fix inaccuracies and improve output.

Apply three techniques: few-shot prompting, chain-of-thought prompting, and role-based prompting to improve a vague prompt, compare outcomes, and choose the best method for the audience.

Explore mindfulness solution breakdown using three prompting techniques: few-shot prompting, chain-of-thought prompting, and role-based prompting. Highlight audience, tone, and output suitability, and conclude role-based prompting best for beginners.

Explore ethical and responsible AI in prompt engineering, identify bias, privacy risks, and misinformation, and learn to detect and mitigate harm using incident data and ethics frameworks.

Explain how ai amplifies biased data and prompts, creating discrimination and misinformation, with real-world gender bias examples and the role of inclusive prompts to improve fairness.

Learn how to apply privacy aware prompts and prompt engineering to prevent leaking sensitive data, personal identifiers, or confidential financial details by using anonymized inputs for compliant AI interactions.

Explore how responsible AI is becoming law with EU AI act and AI principles, emphasizing accountability, fairness, and transparency. Highlight NIST playbooks, measurable requirements, and risk-based evaluation.

Identify biases in AI prompts and refine them to create inclusive, skill-focused, accessible job posts emphasizing collaboration, performance, growth, and accommodations.

Different industries require domain-specific prompting, adapting tone, accuracy, and risk sensitivity. Developers seek accuracy and testability; marketers prioritize tone and compliance; HR and healthcare emphasize fairness, empathy, and source citation.

Review and critique two prompts for ethical flaws, label biases, and propose improved, inclusive prompts to reduce harm; promote role awareness and ethics engineering in prompt design.

Analyze prompts to identify gender and cultural biases and replace them with inclusive, globally aware framing. Describe how to create neutral, privacy-respecting outputs for performance and HR content.

Engage in capstone prompt engineering through real-world katas, applying techniques to automate customer support with empathetic, accurate responses while refining prompts and upholding ethical, private AI usage.

Kata 1 solution demonstrates refining a system message to build a professional customer support persona. It guides users through the cancellation process with empathy and consistency.

Define content goals, select a product, and pick a platform to generate platform-specific marketing content. Refine the initial prompt, evaluate relevance and ethics, ensuring trust, non-deceptive, and culturally sensitive content.

Learn to craft structured API prompts to generate Instagram captions, email headlines, and a product tagline for Aqua Pure, a recycled-glass minimalist water bottle, with eco-conscious tone.

Design a prompt to generate clear, accurate summaries of complex internal reports for quick decision making, highlighting key points and actionable insights, while testing, refining, and protecting confidentiality.

Explore prompt engineering for building concise executive summaries of lengthy internal reports using Colab, the ChatGPT API, and refined prompts with actionable insights and confidentiality considerations.