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prompt Engineering Mastery
Free Download prompt Engineering Mastery
Published 5/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 2m | Size: 2.2 GB
Master ChatGPT, Claude and AI Tools With Proven Frameworks That Get Consistent, Professional Results Every Time
What you'll learn
Understand how LLMs work, including tokens, context windows, and probability, so you prompt with intention rather than guesswork.
Write effective prompts using zero-shot, few-shot, chain-of-thought, and role prompting for real-world tasks.
Control model output using formatting instructions, temperature settings, and structured outputs like JSON and XML.
Apply advanced techniques like self-consistency, ReAct, and RAG for complex, multi-step AI workflows.
Evaluate and iterate on prompts systematically to identify and fix hallucinations, sycophancy, and instruction drift.
Build prompt pipelines for summarization, classification, code generation, and information extraction tasks. You said: What Are the
Requirements
or PrerequisiteRequirements
No programming experience required. This course is built for non-technical learners.
Basic comfort using a computer and typing in a chat interface like ChatGPT or Claude is helpful but not mandatory.
No math or machine learning background needed. Technical concepts are explained from the ground up.
Access to any free AI tool such as ChatGPT, Claude, or Gemini is recommended so you can practice alongside the lessons.
Description
What You Will Learn
Day 1: How Language Models Actually Work Before you write a single prompt you need an accurate mental model of what you are working with. You will learn how tokens, context windows, and probability distributions shape every output you receive, why the model has no memory of your intent and only responds to what you write, and why prompt structure changes model behavior in ways that are predictable and controllable once you understand the mechanics.Day 2: Prompt Anatomy and Core Techniques You will learn the four components every well-formed prompt contains, how to use zero-shot, one-shot, and few-shot prompting to control output quality and consistency, how role prompting and persona setting activate domain-level expertise and precise tone, and how negative prompting closes the gap between an output that is almost right and one that is exactly right.
Day 3: Reasoning and Complex Tasks You will learn how chain-of-thought prompting dramatically improves accuracy on multi-step tasks by making the model's reasoning visible and auditable, how step-back prompting grounds complex answers in principled reasoning rather than surface pattern matching, how to decompose complex tasks into sequential stages that compound quality across every step, and when reasoning chains add value versus when a direct answer is the faster and smarter choice.
Day 4: Output Control and Formatting You will learn how to produce structured outputs in JSON, XML, and Markdown that are immediately usable in automated workflows, how to control tone, length, and style through behavioral definitions rather than vague adjectives, how delimiters and formatting cues act as structural signals that produce more consistent outputs, and how to build reusable prompt templates that encode your best prompting decisions once and apply them every time.
Day 5: Advanced Strategies You will learn how self-consistency and majority voting improve reliability on complex reasoning tasks by running multiple independent responses and selecting the most common answer, how ReAct prompting transforms a language model from a static text generator into a dynamic problem-solving agent that retrieves and acts on real information, how retrieval-augmented generation grounds model outputs in verified source material rather than training data, and how system prompts and user prompts form a two-layer architecture that gives you precise control over model behavior at scale.
Day 6: Evaluation and Iteration You will learn how to measure whether your prompts are actually working using defined quality criteria and representative eval sets, how to build an evaluation framework that converts prompt refinement from an opinion-based process into a measurement-based one, how to recognize and fix the three most common systematic failure modes in production prompt engineering, and how to iterate systematically using hypothesis-driven experiments rather than intuition-based guessing.
Day 7: Real-World Applications and Capstone You will apply everything you have built to the four highest-value task types in applied prompt engineering, code generation, summarization, classification, and information extraction, learn how domain context changes the prompting controls that matter most in legal, medical, and creative writing contexts, build and evaluate a complete prompt pipeline for a real task from your own work, and learn how to stay current as models evolve without rebuilding your entire practice every time a new release appears.
Who this course is for
Professional or Knowledge Worker who uses ChatGPT, Claude, or similar tools daily and wants to get significantly more out of them
Product manager, marketer, or writer building AI-assisted workflows and tired of unpredictable outputs
Developer or engineer who wants a structured foundation before moving into API integration or agentic systems
Educator, researcher, or analyst who needs to extract structured, accurate, and well-formatted responses from AI models
Entrepreneur or founder exploring how to build AI-powered products and needing to understand what prompts can and cannot do
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