Complete Prompt Engineering Practical Course C|PEPC

Design and refine prompts—central to prompt engineering—to interact with AI language models, crafting questions or instructions to elicit accurate, useful responses across applications like content creation, customer service, and education.

Trace the history and evolution of prompt engineering from Eliza’s rule-based chats to modern transformers, GPT, and BERT, highlighting how prompts shape AI outputs with words, images, and sounds.

Explore AI foundations such as artificial intelligence, machine learning, neural networks, NLP, and transformers, including GPT and BERT, and learn 14 key terms from prompting to few-shot learning.

Explore artificial intelligence through how computers learn from examples, adapt in games and daily tasks, and apply AI to problem solving, language understanding, and creative output.

Discover machine learning through a colorful analogy and practical steps: learn from data, find patterns, make predictions, and improve over time, enabling smarter tools and creative problem solving.

Explain the difference between AI and ML with relatable examples like a smart home system and a personalized online recommendation engine.

Explore how neural networks resemble mini brains, with layered learning, training through feedback, and the ability to recognize patterns and make smart computer decisions.

Clarify how AI, ML, and neural networks differ using real-world examples of a smart assistant and photo tagging, highlighting language understanding, data learning, and pattern recognition.

Explore natural language processing, where computers break down language, understand meaning and context, detect emotions, and respond appropriately, enabling voice assistants like Siri and Alexa.

Identify how AI, ML, neural networks, and NLP differ and interconnect through autonomous vehicles and online customer support, highlighting their distinct roles and synergies.

Explore tokenization as breaking sentences into tokens, using spaces and punctuation as separators, enabling tasks such as translation, sentiment analysis, and question answering.

Explore sentiment analysis, teaching computers to read text and detect emotions like positive, negative, or neutral through tokenization and context clues.

Explore tokenization as the first step of text analysis and sentiment analysis as the tool that quantifies emotion in tokenized data, illustrated with reviews and social media examples.

Explore prompting as guiding AI with clear prompts to generate answers, stories, or art. Learn how prompts are received, analyzed, and turned into responses using knowledge.

Parse sentences like a language detective, breaking them into words, subjects, verbs, and objects to build meaning. Discover how computers use this parsing to understand and respond.

Explore how tokenization, sentiment analysis, and parsing differ, illustrated by customer feedback and social media examples, to reveal text processing steps and insights.

Explore how deep learning uses layered neural networks and large datasets to recognize patterns, learn from examples, and power tasks like language translation, driving cars, and medical diagnosis.

Learn the differences between AI, ML, NLP, neural networks, and deep learning through practical examples of a voice assistant and fraud detection, highlighting how each concept learns and interprets data.

Explore supervised learning through a simple teacher-student analogy. See how labeled images train computers to recognize cats and dogs and to identify new pictures.

Explore unsupervised learning by sorting data into groups without labels, using patterns to guide categorization, and see computers learn from data to build AI foundations.

Explore the differences between supervised and unsupervised learning through practical examples: email spam filtering with labeled data and email organization without labels, and customer data analysis with segmentation.

Explore reinforcement learning, where computers improve by trying actions, receiving feedback, and earning rewards. Illustrate how games and bike riding analogies explain decision making and future AI applications.

Explore zero-shot learning, where AI applies broad general knowledge to unfamiliar tasks, making educated guesses and learning from outcomes to improve adaptability and problem-solving.

Explore few shot learning by showing how a computer recognizes new tasks from a few examples, identifying patterns and using prior knowledge to make predictions.

Explore differences between reinforcement learning, zero-shot learning, and few-shot learning with game development and e-commerce examples. See how each method learns from rewards, prior knowledge, or limited data to adapt.

Review key AI and machine learning concepts through a four-column comparison table, outlining terms, descriptions, examples, and how computers understand them to reveal how systems learn and process information.

Explore how a virtual assistant uses machine learning, neural networks, natural language processing, tokenization, and learning paradigms like supervised, unsupervised, reinforcement, few-shot, and zero-shot to answer weather questions for Paris.

Explore the basics of prompt design and how AI language models work, covering how they process language, generate text, and the key prompt types open ended, closed ended, and instructional.

Explore how AI language models learn from vast text to understand and generate language. See how they work with prompts, answer questions, write emails, translate, and power chatbots.

Explore how ai language models understand and generate text through massive data training and pattern and context learning. Predict the next word to enable writing, translation, and educational tools.

Explore how large language models generate responses by word-by-word prediction from vast training data, with context shaping prompts, randomness, and known limits.

Explore major AI models like ChatGPT, Claude, Bard, Gemini, Llama, and Microsoft Copilot, detailing their use cases and world-changing impacts.

Explore the strengths and limits of AI language models, including maintaining context and generating human-like text, with applications in writing and chatbots, and their biases and reliance on input quality.

Master the art of crafting effective prompts by applying clarity, specificity, and context to guide AI toward relevant, accurate responses, balancing detail and prompt length with vague versus clear prompts.

Explore how AI language models use open ended, closed ended, and instructional prompts to guide output. Learn through examples and understand how prompt choice aligns with your objective.

Explore open ended, close ended, and instructional prompts through a practical demonstration, enhancing reading, critical thinking, and understanding of real world responses, with a quiz at the end.

Identify seven common prompt pitfalls and learn practical strategies to avoid them when designing prompts for AI language models.

Explore section three's intermediate prompt engineering techniques, including prompt tuning, refinement, and analyzing model responses. Learn contextual and conditional prompting, logical constructs, and creative prompting for storytelling and scenario building.

Master prompt tuning and refinement through iterative testing and adjustment to tweak words, add context, or change structure, guiding AI responses with precision in education, writing, and data analysis.

Analyze model responses to assess accuracy, relevance, consistency, quality, and tone; refine prompts and improve interaction with artificial intelligence language models through practical evaluation and examples.

Master contextual and conditional prompting by setting contexts through background information and scenarios, and applying conditions to guide artificial intelligence responses to specific needs.

Explore conditional statements and logic in prompt engineering, using if-then rules, branching prompts, and logical constraints to craft context-aware, adaptive artificial intelligence responses.

Explore creative and complex prompting to craft storytelling, role-playing, and scenario-building prompts that push AI language models toward imaginative, detailed responses.

This recap covers intermediate prompt engineering techniques, including fine tuning prompts, iterative testing and refinement, analyzing model responses, setting context, and using conditional logic to craft creative scenarios.

Explore conditional logic in prompts to guide AI responses with if-then scenarios, plus imaginative storytelling and role-playing prompts to craft dynamic, context-aware interactions.

Advance your prompt engineering skills in section four, focusing on chain-of-thought prompting, zero-shot and few-shot learning, ethical considerations, bias and fairness, privacy and data security, and troubleshooting.

Explore advanced prompt engineering with chain of thought prompting, guiding artificial intelligence through step-by-step reasoning. This method improves accuracy and structured problem solving for math problems and puzzles.

Explore zero shot learning and few shot learning, showing how AI uses general knowledge or limited data to adapt to new tasks, including chat-based flight booking scenarios.

Learn how to identify and mitigate bias in ai, ensuring fairness across groups. Explore best practices such as diverse training data, regular audits, transparency, and stakeholder feedback.

Protect privacy and secure data in AI through encryption, access control, and audits; enforce data usage policies and anonymization in customer service and health care.

Master prompt troubleshooting by refining ambiguity and vagueness, adding context, breaking complex queries into simpler questions, and clarifying intent to yield targeted, useful AI responses.

Maximize ai model performance by troubleshooting and optimizing prompts, tailoring them to the model’s strengths and limitations to generate accurate, relevant responses through iterative testing and refinement.

Explore more than 11 prompt patterns to tap the capabilities of large language models, discover how specificity, structure, and context shape outputs, and learn practical refinement techniques.

Learn how to introduce new information to large language models by providing data in prompts. Emphasize context and assumptions with practical examples like weather data, patient vitals, and dietary logs.

Learn how detailed data informs linear regression forecasts across academic, scientific, and business contexts, from enrollment predictions and fertilizer growth to sales trends and financial planning.

Address ambiguity by crafting specific prompts to guide LLMs, and apply clear technical writing for tasks like setting up a Python virtual environment, smart home security, and related installations.

Master prompt size management for large language models by selecting relevant information, summarizing content, filtering irrelevant data, and preserving essential details to craft concise, effective prompts.

Explore how to manage prompt size in large language models through chunking, iterative querying, and metadata to enable advanced data integration and comprehensive analyses.

Explore how root prompts set the rules for large language models, shaping conversation with guidelines to avoid harm and customize behavior, such as a personal assistant offering budget-friendly recommendations.

Route prompts customize LMS with initial prompts for roles like a virtual tutor or shopping assistant, while an ancient history tutor demonstrates focusing on ancient topics and avoiding modern history.

Explore how custom root prompts shape LLM behavior and training cutoffs, using GPT four experiments to illustrate practical, safe, tailored interactions for various applications.

Learn the question refinement pattern to sharpen prompts for large language models like ChatGPT, instructing improvements and implementing iterative refinements for clearer, more productive interactions.

Apply the question refinement pattern across domains to tailor practical, actionable advice for creative writing, math, career, and personal finance.

Deconstruct problems into subquestions using the cognitive verifier pattern to produce precise, actionable answers. Synthesize subanswers to enhance reasoning accuracy and clarity.

Apply the cognitive verifier pattern across fields to deepen user interaction and improve model responses; explore scenarios from starting a business to health and tech for structured, actionable insights.

Apply the audience persona pattern to tailor outputs of large language models to an audience’s background, knowledge level, and interests, producing resonant, accessible content across diverse scenarios.

Master the flipped interaction pattern with large language models and learn how the model asks questions to gather information and reach a goal, enabling personalized plans, troubleshooting, and interactive learning.

Explore how few-shot prompting teaches large language models by showing input-output examples to learn patterns, with real-world tasks like sentiment analysis, translation, and question answering.

Explore how few-shot prompting trains llms to plan and act by learning action sequences from examples, with driving, customer service, health care, and project management use cases.

Explore how few-shot prompting guides autonomous vehicle decisions across driving scenarios and can be expanded to healthcare, customer service, and project management through detailed examples and structured prompts.

Enhance model performance with few-shot prompting and intermediate steps, guiding multi-step reasoning for better accuracy and the ability to generalize across contexts.

Identify common errors in few-shot prompting and learn to correct them with detailed prompts, expanded examples, and clear context to improve large language models' performance across fields.

Learn chain of thought prompting to guide step-by-step reasoning in large language models, improving accuracy, consistency, and transparency by revealing the model’s reasoning process.

Explore real-world applications of chain-of-thought prompting across healthcare, finance, education, legal analysis, science, engineering, and creative writing to improve decision making and task execution.

Discover how react prompting lets large language models use external tools and data sources to reason more accurately, by querying information and performing computations with APIs and web searches.

Evaluate and maintain prompts as llms evolve, ensuring consistency, effectiveness, and adaptability, using llms to self or cross-evaluate outputs with few-shot scenarios.

Engage with prompt engineering through the gameplay pattern and its format to turn learning into an interactive, game-like experience with immediate feedback and structured prompts.

Explore the format of the gameplay pattern to structure prompts, gain immediate feedback, and turn learning into engaging, hands-on games across math, history, coding, and more.

Master the template pattern to shape ChatGPT outputs with a fixed format and placeholders. Define the template, mark placeholders, and keep structure intact to ensure precise, consistent results.

Master the template pattern to guide ChatGPT with placeholders while preserving formatting. Learn setup steps, placeholders, and benefits like consistency and professionalism in prompts.

Learn the meta language creation pattern to design your own shorthand for ChatGPT, enabling faster, code-like prompts and domain-specific communication that streamlines tasks.

Explore the format of the meta language creation pattern to design a concise shorthand for prompts. Define terms, provide examples, and test chat responses to streamline prompt engineering.

Learn the recipe pattern to fill in missing steps in complex prompts, break down tasks, and leverage ChatGPT to complete the plan from partial knowledge.

Learn the recipe pattern to fill in missing steps by stating a goal and listing known steps. Then ask ChatGPT to complete the sequence.

Explore the alternative approaches pattern in prompt engineering to generate multiple solutions. Compare pros and cons, optionally include the original method, and apply this pattern for flexible, creative problem solving.

Explore the alternative approaches pattern to generate multiple solutions and compare their pros and cons. Structure prompts for variety to guide fast, creative problem solving in prompt engineering.

Master the iterative refinement pattern to iteratively improve prompts and outputs with structured feedback, boosting precision, clarity, and creativity across summaries, technical documentation, creative writing, and project plans.

Master the format of the iterative refinement pattern to guide a large language model like ChatGPT through iterative refinements from rough draft to polished final product.

Explore the constraints-based pattern to guide large language models with defined tasks, word counts, formats, and tone, enabling precise, structured outputs.

Learn the constraints-based pattern to guide large language models toward structured, tailored outputs by defining task objectives and explicit constraints, with flexible interpretation, tone controls, and test prompts.

Create an ai powered study buddy that offers study tips, creates study schedules, and answers basic subject questions using prompt engineering, guiding learners through seven steps.

Explore how a demo conversation with an AI study buddy personalizes a study schedule for penetration testing and cyber security, including resources and a three-week timetable.