Zero to Hero Mastering Prompt Engineering

Explore artificial intelligence as an umbrella for automated reasoning, machine learning, and generative ai, with supervised, unsupervised, reinforcement learning, clustering, and multimodal examples like ChatGPT.

Explore foundational models and large language models, from pre-training on vast data to fine-tuning with reinforcement learning, and see how llms perform tasks like chatbots, translation, and summarization.

Learn how temperature controls creativity in LLMs, shifting from deterministic outputs at low temperatures to more random, creative results at higher temperatures, and when to use each setting.

Explore how training epochs improve llms performance by iterating through data, reducing loss, and avoiding overfitting to enable the model to generate new content.

Pre-processing cleans and organizes raw data for AI models; tokenization breaks text into tokens to build a corpus-ready vocabulary.

Discover what prompt engineering is and why it matters, guiding ai outputs with text, images, or audio. See practical examples with Claude AI, learn refining prompts, and anticipate future trends.

Discover how to craft prompts for generative AI using the I craft examples framework, guiding you through instruction, context, role, arrangement, formatting, tone, and examples to design effective prompts.

Explore zero-shot, few-shot, and few-shot chain-of-thought prompting to guide AI reasoning, solve complex tasks, and generate accurate, structured responses across examples like translations and arithmetic.

Master three advanced prompting techniques—generative knowledge prompting, least-to-most prompting, and emotional prompting—to enhance AI interactions with LMS and generate clearer, more relevant outputs.

Explore AI hallucinations, their causes from training data limits and biases, and practical mitigation with self-consistency prompting, roll prompting, retrieval augmented generation, and self-evaluation prompting.

Discusses the risks and ethical challenges of generative AI, including amplified biases, AI hallucinations, copyright disputes, and privacy concerns, with real-world examples and guidance on responsible use.

Master prompt engineering by understanding AI inner workings, large language models, and tokenization. Apply zero-shot, few-shot, and chain-of-thought prompting to craft effective prompts, while mitigating hallucinations and biases.