TLDR;
This comprehensive ChatGPT Full Course 2025 covers everything from the fundamentals of ChatGPT to advanced techniques like prompt engineering, multimodal prompting, and building real-world applications. It also provides tips and tricks for earning money using ChatGPT and acing AI interviews.
- Fundamentals of ChatGPT and its applications
- Prompt engineering techniques for better AI responses
- Building applications using ChatGPT
- Earning money using ChatGPT
- Tips and tricks for ChatGPT
- AI interview preparation
Introduction to ChatGPT Full Course 2025 [0:00]
The course introduces ChatGPT as a versatile tool for content creation, brainstorming, task automation, and data analysis. It highlights the increasing demand for ChatGPT skills in 2025 and emphasizes the course's practical, hands-on approach. The course also mentions a professional certificate program in generative AI and machine learning offered in collaboration with the ENICT Academy IIT Kanpur, providing live interactive sessions, projects, and career support. ChatGPT is described as an advanced language model based on GPT3 technology, trained on a massive dataset, with a new version, GPT4, on the horizon, boasting significantly more parameters.
How does ChatGPT work [2:07]
GPT4 is expected to have one trillion parameters, enabling more accurate and relevant responses. While GPT3 has applications in text generation, summarization, and automated content creation, GPT4 may also generate images and videos. Despite some hype, OpenAI's CEO has cautioned against overstating GPT4's capabilities, clarifying that it is not an actual AGI (artificial general intelligence). The emergence of ChatGPT has impacted Google's search business, prompting a "code red" response focused on developing competing AI products. Microsoft has invested heavily in OpenAI, anticipating the release of GPT4 in early 2023, which may feature multimodal capabilities.
Introduction to Prompt Engineering [5:49]
Prompt engineering is the art and science of crafting effective questions or prompts to elicit the best responses from AI. A vague prompt yields vague results, while a well-structured prompt leads to clear, relevant, and targeted responses. Prompt engineering involves designing questions or instructions that enable AI to generate accurate and useful outputs. It is analogous to providing clear directions to a GPS for accurate navigation.
Prompt Engineering applications [28:56]
Prompt engineering has real-world applications in customer support, coding, education, marketing, and data analytics. AI understands prompts by predicting the most likely next words based on patterns learned from training data. Tokenization, breaking down sentences into smaller pieces or tokens, helps AI understand text better. AI has a context window, acting as working memory, which affects its ability to remember previous parts of a conversation.
ChatGPT for Programming [1:24:16]
A well-constructed prompt includes a clear task, target audience, context or role, and desired formatting. Strong prompts provide clear direction to the AI, resulting in more useful and focused responses. The demo shows the difference between weak and strong prompts, illustrating how specific prompts yield better results. Examples include improving a vague prompt about climate change, providing missing context for a productivity post, using step-by-step prompting for a blog post, and employing role-based prompting for summarizing an article.
Multimodal prompting for beginners [2:59:25]
Prompt engineering directs AI models to perform their best by asking specific questions. Effective prompts should be specific, provide context, focus attention, and iterate as needed. Prompt engineering is applicable in content creation, customer support, software development, education, market research, healthcare, and legal compliance. AI technologies flow from AI to ML to deep learning to LLMs, with LLMs being specialized for understanding and generating human language.
Context Engineering [3:20:03]
To create a precise prompt, include context, task, persona, format, example, and tone. Examples include creating a presentation for a nutritionist, writing a short story as a sci-fi author, and preparing a financial guide as a financial advisor. For content creation, prompts can ask for blog posts or articles, specifying the persona and desired tone. For SEO, prompts can request presentations explaining SEO basics, with the AI acting as an SEO expert. For developers, prompts can ask for tutorials or debugging assistance, with the AI acting as a software engineer.
Vibe Coding [3:35:16]
For data analysis, prompts can ask for pivot tables or summaries, with the AI acting as a data analyst. For education, prompts can request learning roadmaps, with the AI acting as an experienced educator. For legal and compliance, prompts can ask for compliance checklists, with the AI acting as a legal advisor. In healthcare, prompts can request recipes or diet plans, with the AI acting as a dietician. For customer support, prompts can ask for training modules, with the AI acting as a customer service trainer.
Reasoning with open ai o1 model [3:44:36]
To create PowerPoint presentations, prompts can ask for VBA code, with the AI acting as a presentation specialist. Key features of ChatGPT include customized instructions, memory, data controls, connected apps, and a builder profile. The memory feature captures and summarizes key information from interactions, allowing ChatGPT to adapt over time. Temporary chat allows conversations without storing memory. The video also explores the use of GPT4 for various tasks, highlighting the importance of clear, well-structured prompts.
Prompt Formulae ChatGPT [4:46:07]
Programming is increasingly important and ChatGPT can assist programmers by offering programming-related answers and solutions. ChatGPT works using transformer architecture, generating responses by predicting the most likely next words based on the input. It is utilized for code generation, code completion, code review, and a natural language interface. ChatGPT can generate code, complete code snippets, analyze code for bugs, and enable users to communicate with software applications through natural language instructions.
How to build a website using ChatGPT [5:01:04]
ChatGPT can generate code for a travel website, providing steps to define the scope and features of the website. It can also provide HTML, CSS, and JavaScript files. The video tests ChatGPT's ability to solve difficult questions from LeetCode, a platform for coding challenges. ChatGPT was unable to solve the first question, median of two sorted arrays, and the second question, zigzag conversion, in one go. However, it was able to solve the third question, substring with concatenation of all words, and the fourth question, N queens, after some modifications.
Content Creation using ChatGPT [5:44:11]
ChatGPT was able to solve the fifth question, shortest subarray with sum at least K, after multiple attempts. It failed to solve the sixth question, split array with same average, and the seventh question, find substring with given hash value. The video concludes that while ChatGPT is an amazing tool with a bright future, it still has limitations and may not be ready to replace humans. It can generate logics and approaches for code effectively, but its ability to analyze questions is weaker compared to humans.
Build App Using ChatGPT [5:59:00]
Multimodal prompting allows AI models to interpret and respond to input from multiple modalities like text, images, and audio. It is used in applications like image captioning, video analysis, interactive chatbots, and healthcare diagnostics. Prompting has evolved from text-only models to multimodal models that can interpret and connect across different types of information. Modern multimodal models have specialized architectures and are trained on paired data to learn how different modalities relate to each other.
PowerPoint using ChatGPT [6:31:12]
Modalities are different types of information like text, images, sound, or video. Multimodal prompting involves multiple types of input, requiring the model to integrate different streams of information. Challenges in multimodal prompting include misaligned inputs, hallucinations, biases, input format issues, limited modal support, ambiguous inputs, and high computational load. Tools and platforms that support multimodal prompting include ChatGPT with vision, Gemini by Google, Claude by Anthropic, and DALL-E by OpenAI.
How to Earn Money using ChatGPT [6:44:42]
Use cases and applications of multimodal prompting include diagram analysis, content generation, product tagging, interactive education, and accessibility. To get started with multimodal prompting, choose a platform, install required tools, obtain API keys, format inputs, and test with basic prompts. The demo showcases ChatGPT's ability to generate images from text prompts, analyze images, and solve mathematical problems. It also demonstrates how to generate a complex image based on a description.
Sell Ebooks using ChatGPT [7:03:08]
Vibe coding, relying on intuition, often leads to hallucinated APIs, lack of scalability, and brittle tests. Context engineering, providing AI with rules, data, memory, tools, and desired output, leads to more reliable systems. Prompt engineering focuses on asking the right question, while context engineering focuses on everything around the question. Good context includes system instructions, user input, short-term memory, long-term memory, knowledge bases, and workflow state.
ChatGPT tips and tricks [7:29:01]
Common problems with context include too much information, information overload, wrong order of information, multiple sources, and messy memory. Solutions include summarizing information, structuring text, prioritizing information, using memory blocks, and managing memory. The demo shows how to use custom GPTs to add more context and generate more detailed project plans. It also showcases AI coding tools like Pythagora, Bold, and Lovable.
AI Deep learning Interview Questions [7:51:28]
O1 is OpenAI's reasoning model designed to think logically, self-correct, and explain its thinking step by step. It uses chain of thought reasoning, reinforcement learning, and reasoning tokens. O1 is useful in maths, science, coding, research, and creative work. It avoids jumping to conclusions, ignoring details, and repeating errors. Learning how O1 works helps you use AI more effectively and prepare for a world where thinking with AI is essential.
