Prompt Engineering for Beginners: From Zero to Expert

Explore how ChatGPT handles logical reasoning questions by breaking down complex problems step by step, solving classic reasoning puzzles, and demonstrating clear, critical thinking.

Test deductive reasoning with questions that reveal how ChatGPT follows premises to reach conclusions, like ranking heights and tea or coffee preferences, and visualize results with mermaid diagrams.

Explore how prompts guide LLMs like ChatGPT to solve mathematical reasoning problems, using geometric progression, tables, and graphs to reveal patterns, prompt engineering, and worst-case scenarios.

This lecture tests llm pattern recognition with geometric progression tasks and code generation to extend series, showing how ChatGPT recognizes sequences, explains Fibonacci-based letter-number patterns, and discusses hallucination risks.

Examine common sense reasoning with ChatGPT, testing how LLMs understand human behavior, safety, and decision making via real-world scenarios such as buy-one-get-one offers and ethical dilemmas.

Explore temporal reasoning, time zone conversions, and duration calculations through practical questions, showing how LLMs handle sequences, time constraints, and context switches in real-world scenarios.

Explore how large language models simulate causal reasoning to map cause-and-effect relationships and predict outcomes. Analyze sleep's impact on memory consolidation and focus, and discuss AI advancement toward machine consciousness.

Explore how spatial reasoning works and how large language models approach space tasks by evaluating desk arrangements and floor plans, highlighting limits and practical design applications.

Master the five key elements of effective prompting—clarity, specificity, intent, tone, and structure—for beginners in prompt engineering to guide ChatGPT conversations and achieve precise outcomes.

Explore how specificity shapes prompts to elicit precise responses from language models, using a concrete 150-word email offering a 20% discount on graphic design services.

Define intent as the guiding element of prompts to specify the desired result, explaining, summarizing, persuading, analyzing, or brainstorming, and avoid generic outputs.

Discover how tone shapes prompts through word choice, sentence structure, and style to fit audience and context, illustrated by a friendly, customer-centric product returns FAQ.

Compare unstructured and well-structured prompts to ensure content is organized, coherent, and aligned with the request; explore the canvas feature for editing, paraphrasing, and running code to improve outputs.

Explore the five core prompt components—context, task, input data, output format, and constants—and learn to set context via role or scenario with practical examples for LLMs like ChatGPT.

Define the task description in prompts, instruct the llm to summarize text, and use practical examples to generate concise one-sentence, two-sentence, or paragraph summaries.

Learn how to classify text by labeling it into predefined categories using LLM prompts. Explore examples with positive, negative, and other labels to harness ChatGPT for accurate text classification.

Learn to craft translation prompts for ChatGPT, exploring how to translate text into over 50 languages, including English to German, with practical steps and real-world examples.

Explore text completion and text extension as core prompt tasks, showing how large language models generate next words or expand ideas for auto completion and content expansion.

Explore text extension to turn an initial sentence into a full blog post, story, or essay using prompts and examples.

Identify the five core components of language-model prompts—context, task, input data, output format, and constraints—and see how they guide prompt design to optimize prompts.

Explore zero-shot, one-shot, and few-shot prompting techniques for language models, using examples to guide outputs and understand how tokens, prompts, and context shape results.

Explore chain of thought prompting to guide language models through step-by-step reasoning, generating intermediate steps and clearer, more accurate answers. See how to trigger step-by-step reasoning for problems.

Explore self-consistency prompting and how it differs from input-output and chain-of-thought prompting. Repeat prompts to select the most frequent answer, with and without intermediate reasoning, illustrated by a diagram.

Explore self-consistency prompting with chain-of-thought, generate multiple reasoning paths, and identify the most consistent answer to solve complex problems.

Learn tree of thought prompting, a non-linear reasoning method for large language models that explores multiple branches, evaluates paths, and prunes unpromising ones to find the best solution.