Prompt Engineering for Everyone: Master AI & ChatGPT

Explore the components of a large language model—recurrent, feedforward, embedding, and attention layers—and learn how they enable natural language processing and generation in transformer architectures.

Explore the Transformer model's encoder-decoder structure and self-attention, enabling parallel processing and accurate language understanding for translation, summarization, and text generation.

Explore the benefits of large language models, including language translation, sentiment analysis, and question answering. Learn how LLMs continuously improve and train rapidly to power AI-driven solutions.

Explore popular large language models like BERT, POM, and ChatGPT, and their bidirectional understanding, Pathways architecture, and ability to generalize across millions of tasks for translation, chatbots, and text generation.

Explore how large language models power understanding and generation of human language, guided by transformer architectures and massive training data; apply recognition, translation, and text generation across real-world scenarios.

Explore how large language models recognize, summarize, classify, translate, predict the next word, and generate text to power versatile natural language processing tasks.

Explore the challenges of hallucination in large language models and learn to mitigate them through effective prompt engineering and verification.

Explore integrating chat gpt-3 with a custom engine by importing OpenAI, entering an API key, and using the openai create method to generate prompts and responses.

Build a chat bot in Colab by installing libraries, configuring a GPT-2 model, and training with a prompt–response dataset to generate and evaluate answers.

Explore how to craft effective prompts for ChatGPT by comparing three types—transform or clarify prompts, Q&A prompts, and creative prompts—and applying best practices like specifying goals, audience, and output format.

Learn to craft effective prompts using a simple prompt template that assigns a role, defines a clear task, and provides context to turn vague queries into precise, high-quality ChatGPT outputs.

Apply prompt anatomy to design prompts with role, goal, formatting, constraints, and context, guiding chatgpt to produce exact, structured outputs.

Apply the prompt formula to structure prompts with a role, goal, format, constraints, and context. Follow a fill-in-the-blanks pattern to guide ChatGPT and improve accuracy and creativity.

Master prompt expansion techniques to sharpen ai outputs using iterative prompting, chain-of-thought reasoning, clarifying intent, add examples, remove buzzwords, and multi-step prompts with storytelling tone for executives.

Master advanced prompting techniques such as few-shot prompting, N-shot prompting, chain-of-thought prompting, and role prompting to boost accuracy, creativity, and reasoning with ChatGPT.

Explore essential tools for prompt engineering, including Promptero, FlowGPT, ChatGPT plugins and GPTs, GPT Builder, Zapier automations, and Chrome extensions Merlin and AIPRM, to streamline workflows and automate tasks.