Prompt Engineering Mastery: Build AI, Bots & Automation

Explore tokenization and embeddings that transform text into machine readable input for language models, enabling context understanding and generation through pre-trained models and transfer learning.

Explore the transformer architecture and attention mechanisms that power language models, including self-attention, multi-head attention, tokenization, and encoder-decoder design.

Explore how AI language models turn prompts into coherent text by tokenizing input, leveraging pre-training knowledge, applying attention, predicting next words, and refining prompts for clarity and creativity.

Explore GPT, BERT, T5, and other NLP models, compare unidirectional vs bidirectional approaches, and review their applications from text generation to question answering and translation.

Explore the anatomy of a prompt—instruction, context, example, and constraints—and learn to craft clear, specific prompts for creative, business, and technical tasks.

Master how to use examples to craft precise prompts, reduce ambiguity, guide tone and style, and apply direct, implicit, and counterexamples with best practices and common pitfalls.

Explore chain of thought prompting, few-shot learning, and zero-shot learning to guide AI through step-by-step reasoning, learn their differences, and apply these techniques to multi-step problems.

Master advanced prompt engineering techniques to craft fine-tuned prompts, handle multi-step tasks, and iteratively improve AI responses with detailed context, step-by-step structures, and clear output formats.

Contextualize prompts for a specific purpose by adding background information, setting boundaries, and focusing on key aspects. Customize AI responses by tone, style, form, and length for precise, relevant outputs.

Craft prompts tailored to specific outputs by selecting formats such as bullet points, paragraphs, and lists, while managing knowledge cutoffs, biases, and other ai limitations to ensure clear, readable results.

Iteratively refine prompts and adjust model parameters to control creativity, accuracy, and clarity, testing temperature, max tokens, top P, and penalties for consistent, relevant AI outputs.

Mitigate bias and evaluate prompt effectiveness by designing careful prompts, using diverse data, and setting up feedback loops and automated evaluation for continuous improvement.

Explore how bias arises in AI from training data, algorithms, and sampling, and learn prompt design strategies to mitigate bias and foster fair, accurate outputs.

Promote fairness and inclusivity in prompt engineering by designing unbiased prompts, while examining transparency, interpretability, and compliance with GDPR and CCPA for responsible AI.

Explore real world prompt engineering applications in AI content creation, AI chatbots for customer service, and AI driven business intelligence and data automation, including best practices.

Explore real world applications of prompt engineering across healthcare, finance, education, and software development, and learn how precise prompts drive AI insights, automation, and faster coding.

Explore large language models and craft effective prompt patterns through roles, audience awareness, structure and constraints, few-shot examples, and iterative refinement in lab one.

Build a persistent personal AI writing assistant with Google Gemini Gems, applying a four-step workflow—clarification phase, brainstorming phase, drafting phase, and variation phase—to produce structured, high-quality LinkedIn posts.

Build an ai powered faq bot from a csv using Google Gem, converting product data to markdown, configuring a custom gem with prompts, and testing for accuracy.