Generative AI and Prompt Engineering Training

The most expensive AI degree in the world... is now free. And it’s just a click away. Harvard University has released 6 lectures on AI and prompt engineering, no paywall, no sign-up, just world-class insights. As someone who’s spent two decades leading AI strategy, data governance, and digital transformation across legal tech, public service, and regulated sectors… I can tell you this: 📌 Prompting is the new executive literacy. But most professionals are still stuck at the surface level, tinkering, not transforming. If you're serious about building AI fluency (for yourself or your team), these lectures are your starting point. I've included the direct links below. And added 4 bonus lectures to take you deeper. HARVARD LECTURES 1. Introduction to Generative AI A step-by-step guide to how GenAI actually works. Link: https://lnkd.in/e9rWNjBE 2. Prompt Engineering Real tips for improving output quality from any LLM. Link: https://lnkd.in/e5nekvS2 3. Beyond Chatbots: System Prompts, RAG Move past surface use cases into scalable applications. Link: https://lnkd.in/exsX5tP7  4. Generative AI in Teaching & Learning How educators can adapt and lead with AI. Playlist: https://lnkd.in/erdwPRvu 5. Teaching with AI in the Classroom Frameworks for trainers and educators. Link: https://lnkd.in/eWXskwWG 6. The Basics of Generative AI No jargon. Just clarity. Link: https://lnkd.in/eSCJ62Bi BONUS DEEP DIVES 🟩 CS50x 2025 – Artificial Intelligence Lecture LLMs, neural nets, and real-world use cases Link: https://lnkd.in/eYg6vdCr 🟩 CS50 Extension – AI / Prompt Engineering Design prompts that think with you Link: https://lnkd.in/eJH4DWQR 🟩 GPT-4: How it works + how to build with it Behind the curtain on GPT-4 Video: https://lnkd.in/eeh7QXUM 🟩 LLMs and the End of Programming Why prompting is the new coding Video: https://lnkd.in/e3JbSFi7 Key Takeaways: ✅ Harvard-level prompting knowledge is now free ✅ Great prompting isn’t optional, it’s leverage ✅ Watch 2 lectures and you’ll be miles ahead of most execs ✅ The future belongs to those who can communicate with machines strategically Which lecture will you start with? Drop it in the comments.👇 ________________________________________ ♻️ Repost to support your network. 🔔 Follow Dr. Kion Ahadi for insights at the cutting edge of knowledge.

Unlock the potential of Generative AI to enhance your writing, creativity, and coding skills through prompt engineering. Prompt engineering is a key skill that involves crafting detailed, structured inputs to guide AI towards generating precise, useful outputs. Here are the core strategies to master: - Guide Precisely: Provide detailed instructions for clear, targeted outcomes. - Rich Context: Supply comprehensive background information for more accurate and relevant responses. - Experiment: Start with the basics, then explore more complex requests as you become more comfortable. Improve your AI interactions with these tips: 1. Specificity and Iterations: Craft detailed prompts and refine based on the AI's feedback. 2. Contextual Depth: The more context you provide, the better the AI understands your request, leading to more tailored outputs. 3. Multi-Modal Inputs: Beyond text, incorporate images, code, or data for varied and rich outputs. 4. Example Use: Include examples of what you're aiming for and what you want to avoid to guide the AI more effectively. 5. Advanced Features: Tweak settings like creativity level and response length to get the results you need. 6. Unique Capabilities: Utilize the AI's broad knowledge and support for specific tasks, such as coding assistance. ✍️ Suppose you want to learn a new skill. Here's a prompt template incorporating the above principles: 'I'm eager to learn [Skill Name], aiming to use it for [specific purpose or project]. My background is in [Your Background], and my experience with similar skills is [Your Experience Level]. I aim to build a foundational understanding and complete my first project within [Timeframe]. Could you provide a structured learning path that includes: The key concepts and fundamentals of [Skill Name] I should focus on. Recommendations for online courses, tutorials, and books suitable for beginners. Practical exercises or projects for applying what I learn. Tips for staying motivated and overcoming challenges. Strategies for applying [Skill Name] in real-world situations or job opportunities.' This approach ensures a personalized, goal-oriented learning strategy, leveraging AI's capabilities to support your journey in mastering a new skill. #generativeai #ai #promptengineering #upskill #learning

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 just released one of the best free Generative AI roadmaps on the internet. If you want to build with GenAI, not just prompt ChatGPT, this is pure gold. It’s a 19-lesson YouTube playlist + a hands-on GitHub repo that teaches Generative AI from first principles. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝘆 𝗶𝘁 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁 👇 ↳ Starts from what Generative AI and LLMs actually are, not buzzwords ↳ Explains how different LLMs work and compare ↳ Covers Responsible AI (something most courses skip) ↳ Teaches Prompt Engineering from basics to advanced ↳ Shows how to build text generation apps ↳ Builds chat applications step by step ↳ Introduces vector databases & search apps ↳ Every concept is backed by code, notebooks, and examples 𝗔𝗻𝗱 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗽𝗮𝗿𝘁:  → The GitHub repo is insanely practical. → Each lesson has explanations, diagrams, and runnable code you can actually learn from. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗮: ↳ Student ↳ Developer ↳ Data Scientist ↳ ML / AI Engineer 𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸 𝘁𝗵𝗶𝘀. 𝗖𝗹𝗼𝗻𝗲 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼. 𝗙𝗼𝗹𝗹𝗼𝘄 𝘁𝗵𝗲 𝗽𝗹𝗮𝘆𝗹𝗶𝘀𝘁 𝗹𝗲𝘀𝘀𝗼𝗻 𝗯𝘆 𝗹𝗲𝘀𝘀𝗼𝗻. This is how you build strong GenAI foundations in 2025. 🔗 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁: https://lnkd.in/gx54-vvM 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/e-S6r4fE 𝗧𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗳𝗼𝗹𝗹𝗼𝘄: Sebastian Raschka, PhD Chorouk Malmoum Sahn Lam Mary Newhauser Victoria Slocum Want to stay updated on the latest AI Tools and AI Agents? Join my free AI WhatsApp community 👇 https://lnkd.in/epHZYb-j #GenerativeAI #LLMs #AIEngineering

𝗚𝗼𝗼𝗴𝗹𝗲 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝟳𝟭-𝗽𝗮𝗴𝗲 𝗚𝗲𝗺𝗶𝗻𝗶 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗚𝘂𝗶𝗱𝗲. If you work anywhere near LLM pipelines, evaluation, or production AI systems, this is worth your time. Most users are still prompting like it's 2022: → "Write this" → "Summarize that" Zero structure. No role framing. No output constraints. No chain-of-thought scaffolding. Then they wonder why the model hallucinates or returns surface-level completions. 𝗧𝗵𝗲 𝗴𝘂𝗶𝗱𝗲 𝗯𝗿𝗲𝗮𝗸𝘀 𝗱𝗼𝘄𝗻 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗼𝘃𝗲𝘀 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱𝗹𝗲: ↳ A reusable prompt engineering framework. System instructions, few-shot exemplars, and structured output formatting in one coherent pattern. ↳ Role-grounded prompting across real workflows. Marketing, sales, ops, data analysis. Not toy examples. Production-adjacent use cases. ↳ Prompt decomposition for clarity and precision. How to break complex tasks into atomic instructions so the model doesn't have to guess your intent. ↳ Iterative refinement over first-draft prompting. Your v1 prompt is a hypothesis, not a solution. The guide makes the case that prompt iteration is the highest-leverage skill most teams are still ignoring. 𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝘁𝗵𝗲𝗼𝗿𝘆. It's a grounded, immediately applicable playbook. Whether you're building RLHF annotation pipelines, designing evaluation rubrics, or just trying to get tighter completions out of Gemini in your daily workflow. Better prompt design = fewer hallucinations, stronger grounding, higher task completion rates. If you're doing any HITL evaluation, safety annotation, or prompt-driven automation, the section on structured output constraints alone is worth the read. 🔗 Full guide, direct from Google (PDF, no gate, no signup): https://lnkd.in/dVciKxys ♻️ Repost if your team could use this. 📌 Follow for more AI engineering field notes. #PromptEngineering #GenerativeAI #LLM #Gemini #AIEngineering #MachineLearning #RLHF #NLP #LargeLanguageModels #AIEvaluation #HumanInTheLoop #AIProductivity

Prompt Engineering in 2025: The Skills Every AI Professional Must Master Prompt Engineering is no longer just a “nice-to-have”—it’s a core capability for AI Product Managers, Data Leaders, and anyone building with LLMs. According to Google’s Prompt Engineering guide writing effective prompts is an iterative discipline, and the difference between an average prompt and a great one can determine accuracy, creativity, cost, and safety of AI systems. Here are the essentials every professional should know: 🔹 1. Master LLM Output Controls The guide strongly emphasizes tuning model configurations—not just the prompt. Key levers include: ◾ Temperature → controls randomness ◾ Top-K / Top-P → controls diversity ◾ Max Tokens → controls cost + verbosity 🔹 2. Use Powerful Prompting Techniques Modern prompting goes far beyond simple instructions. Top techniques highlighted in the guide: ◾ Zero-shot / One-shot / Few-shot examples ◾ System + Role + Context prompts ◾ Chain of Thought (CoT) for reasoning ◾ Step-Back Prompting for better accuracy ◾ ReAct for agentic behavior (reason + act) ◾ Tree of Thoughts for multi-path reasoning Automatic Prompt Engineering (APE) for self-improving prompts 🔹 3. Best Practices for Writing Better Prompts Directly from the guide’s recommendations: ◾ Keep prompts simple, specific, and explicit. ◾ Use instructions (“Do X”) instead of constraints (“Don’t do Y”). ◾ Provide clear examples, especially for structured outputs like JSON. ◾ Use variables in prompts for reusability. ◾ Mix examples to prevent pattern-bias in classification tasks. Treat prompt design as an experiment-driven process: document, iterate, refine. 🔹 4. Code, Debugging & Multimodal Prompts Beyond text, modern LLMs can: ◾ Generate and explain code ◾ Translate code (e.g., Bash → Python) ◾ Debug broken scripts ◾ Interpret images, UI layouts, and more Writing effective prompts unlocks the model’s full multimodal capability. From temperature tuning to Chain-of-Thought, Step-Back reasoning, and ReAct agents — mastering prompts is now essential for building accurate, safe, and reliable AI systems. #PromptEngineering #GenerativeAI #AIProductManagement #LLM #AIAgents #VertexAI #GoogleAI #ArtificialIntelligence #AIMastery #TechLeadership

In the rapidly evolving landscape of artificial intelligence, the art of crafting prompts has become a crucial skill for developers and researchers alike. As AI systems, particularly large language models, become more sophisticated, the ability to guide these models effectively through carefully designed prompts can significantly enhance their performance and reliability. This article is designed to take you on a comprehensive journey through various prompt engineering techniques. We begin with the basics of zero-shot prompting, where the AI attempts to generate responses without any prior examples. From there, we’ll explore few-shot prompting, where the model is given a handful of examples to guide its output. As we progress, we’ll delve into more advanced strategies like the Chain of Thought technique, which encourages the model to break down complex problems into manageable steps. Finally, we will introduce the ReAct (Reasoning and Action) framework, a powerful approach that facilitates a dynamic interaction between the AI and the user, allowing for a back-and-forth exchange that mimics human reasoning. By the end of this article, you’ll have a deep understanding of how to effectively engineer prompts to harness the full potential of AI, culminating in the mastery of the ReAct framework for nuanced and interactive AI experiences.

We are entering a phase where 𝘬𝘯𝘰𝘸𝘪𝘯𝘨 AI isn’t enough — 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 and 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 powerful, responsible AI systems will set you apart. To help navigate this rapidly evolving landscape, here’s a structured 𝟵-𝘀𝘁𝗮𝗴𝗲 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 to mastering Generative AI in 2025: → 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜: Understand the real differences between AI, ML, and DL. Master the fundamentals like optimizers, activation functions, and gradient descent. → 𝗗𝗮𝘁𝗮 & 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: High-performing AI starts with high-quality data. Learn how to clean, normalize, tokenize, engineer features, and balance datasets for better model accuracy. → 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀): Go deeper than just using GPTs. Study how transformers work, what positional encoding means, and how scaling laws govern large models. → 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Learn how to design effective prompts, create structured prompt chains, manage token budgets, and optimize model outputs systematically. → 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 & 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: Master advanced techniques like PEFT, LoRA, and RLHF to fine-tune and optimize models with minimal data and efficient resource usage. → 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 & 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: Expand beyond text to images, audio, video, and cross-modal generation. Understand diffusion models, captioning, and multimodal search. → 𝗥𝗔𝗚 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Learn how retrieval-augmented generation (RAG) systems ground models with external knowledge. Explore vector databases like Pinecone, ChromaDB, and FAISS. → 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 & 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜: Identify biases, ensure transparency, and integrate responsible AI practices into your systems — because trust and accountability are not optional. → 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲: Turn prototypes into production-grade systems. Focus on API serving, scaling, inference optimization, logging, and setting usage controls. Each stage is mapped with the most relevant 𝘁𝗼𝗼𝗹𝘀, 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀, and 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 to focus on. The world does not just need more AI models. It needs 𝗯𝗲𝘁𝘁𝗲𝗿, 𝘀𝗮𝗳𝗲𝗿, and 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱-𝗿𝗲𝗮𝗱𝘆 AI systems. Built by those who deeply understand the full lifecycle from idea to deployment. → 𝗦𝗮𝘃𝗲 𝘁𝗵𝗶𝘀 𝗿𝗼𝗮𝗱𝗺𝗮𝗽. → 𝗥𝗲𝗳𝗹𝗲𝗰𝘁 𝗼𝗻 𝗶𝘁. → 𝗨𝘀𝗲 𝗶𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗺𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹 𝗶𝗻 𝟮𝟬𝟮𝟱 𝗮𝗻𝗱 𝗯𝗲𝘆𝗼𝗻𝗱.

Most people think you need tuition, credentials, or a sabbatical to learn how AI actually works. You do not. You need curiosity and the discipline to think clearly. Over the past 12 months, I’ve had the unique opportunity to guest lecture and keynote in partnership with Harvard University, and this year Harvard also released a series of generative AI and prompt engineering courses that anyone can access at no cost. That combination matters more than people realize. What I saw repeatedly while working with students and faculty is that the future of AI leadership has very little to do with tools and everything to do with judgment. Prompting is not clever wording. It is structured thinking. The quality of output reflects the clarity of intent, the assumptions built into the system, and the discipline of the person guiding it. AI fluency is quickly becoming part of the executive baseline. Strategy, workforce planning, education, and organizational design are already being shaped by these systems. Leaders do not need to become engineers, but they do need to understand how decisions are translated into outcomes. Harvard has made the following courses publicly available: 1. Introduction to Generative AI https://lnkd.in/gfvpRbsA 2. Prompt Engineering https://lnkd.in/gHfXdRKB 3. Beyond Chatbots: System Prompts and Retrieval Augmented Generation https://lnkd.in/gNkZebz6 4. Generative AI in Teaching and Learning https://lnkd.in/gvPUfsiN 5. Teaching with AI in the Classroom https://lnkd.in/grCn38KY 6. The Basics of Generative AI https://lnkd.in/gEyptr9w What stood out to me throughout this year is that education remains the stabilizing force during periods of disruption. When leaders invest in understanding rather than shortcuts, they make better decisions and create space for responsible innovation. I am grateful for the opportunity to contribute to these conversations and to partner with institutions that take seriously the responsibility of shaping how leaders think as we head into 2026.

Stop waiting for your syllabus to include Generative AI. By the time it’s in the textbook, the industry will have moved on twice. ⏳ To maximize your success in the Generative AI (GenAI) field, here are 8 vital tips for bridging the skills gap and building your professional portfolio. * Strengthen Your Foundation: Master Python (libraries like NumPy and Pandas) and core mathematics (linear algebra, calculus, statistics). This is essential for grasping how models work. * Learn Core AI Concepts: Deeply understand Machine Learning and Deep Learning fundamentals. Focus specifically on Transformer architecture and self-attention mechanisms—the building blocks of modern LLMs like GPT. * Practice Prompt Engineering: Move beyond basic queries. Experiment with zero-shot, few-shot, and Chain-of-Thought (CoT) prompting to optimize Large Language Model performance. This is crucial for controlling model output. * Master Key APIs and Frameworks: Gain experience integrating APIs from OpenAI (GPT-4), Anthropic (Claude), and Google (Gemini). Master the Hugging Face ecosystem (Transformers, Diffusers) and development frameworks like LangChain and LlamaIndex. * Build Practical Projects: Theory isn't enough. Create a visible portfolio by building a chatbot, an image generator, or finely tuning a small model on a custom dataset. Contribute to open source on GitHub. * Stay Current with Research: Read foundational papers on ArXiv and follow industry leaders on social media. AI moves fast; you must be proactive in tracking new trends and models. * Focus on AI Ethics: Understand bias in datasets, copyright issues, data privacy, and model misuse. Knowledge of responsible AI is vital for creating safe, ethical applications. * Collaborate and Network: Join online forums (Discord, Reddit), attend hackathons, and connect with peers. Engaging with AI communities accelerates learning and leads to career opportunities. #GenAI #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #AICareer #PromptEngineering #PythonProgramming #HuggingFace #TechSkills #Innovation #AIResearch #LearnAI #CareerAdvice

📘 Prompt Engineering for LLMs — From “Hack” to Engineering Discipline One of the biggest challenges in Generative AI is reliability. How do you get an LLM to produce the right output — consistently and predictably? This guide shows that the answer isn’t clever wording, but structured prompt design. It treats prompting as an engineering practice, offering a practical roadmap to improve accuracy, reasoning, and control in real-world LLM applications. Key takeaways include: • How LLMs interpret instructions and context • When to use few-shot vs. chain-of-thought prompting • Techniques to reduce hallucinations in RAG systems and agents Whether you’re a student learning GenAI, a developer using LLM APIs, or an engineer building production AI systems, this resource helps you move from basic prompting to intentional system design. #PromptEngineering #GenerativeAI #LLMs #AIEngineering #MachineLearning #ArtificialIntelligence #LearningResources