Generative AI in a Weekend: Learn ChatGPT and OpenAI

Explore technology with chatgpt and identify top cloud engineering technologies, from cloud platforms and containers to infrastructure as code and security, plus docker exercises and prompt-driven insights.

Compare machine learning, which learns from millions of examples to build predictive models, with traditional programming that relies on hand-written rules, and distinguish narrow AI from general AI.

Learn how machine learning uses features and labels to make predictions, distinguish regression from classification with real-world examples like house price, used car price, spam detection, and loan decisions.

Generative AI learns from examples to create new content, distinguishing it from AI and machine learning. It covers text, code, and image generation as core use cases.

Learn how generative artificial intelligence text models use tokens instead of words to handle context, meanings, and relationships, and why token limits require splitting long content.

Explore how generative AI models predict the next word from information learned from internet data. See how supervised fine tuning with labeled prompts and responses trains models to answer questions.

Discover foundation models and large language models, like GPT, that are pre-trained multitask systems for chat, review analysis, and article summarization, with multimodal capabilities across text, video, audio, and image.

Discover how to use the OpenAI Playground for text summarization, login and model basics, and practical prompts. See examples like summarizing course descriptions, reviews, and generating hashtags.

Explore prompt design fundamentals to craft effective prompts for foundation models like ChatGPT. Apply best practices—clear instructions, examples, and experimentation with prompt design frameworks such as RTF, CTF, and RASCEF.

Explore prompt frameworks like RTF, CTF, and RASCEF to craft precise prompts by defining role, persona, context, action, steps, format, and examples.

Generate a top three cloud and DevOps trends using the OpenAI Playground. Then run the same prompt from Python in Google Colab, configuring the OPENAI_API_KEY and using the ChatCompletion API.

Learn to build a chat bot with the OpenAI API’s chat completion, using system messages and examples to control behavior and reduce hallucinations for customer service and education.

Learn to tune large language models by creating a jsonl training dataset, uploading it to Google Colab, and running a fine-tuning job on gpt-3.5-turbo to produce a customized model.

Explore embeddings as vector representations in a high-dimensional space that capture semantic relationships and contextual information, then measure text similarity for recommendations, clustering, and outlier detection.

Explore LangChain fundamentals by configuring OpenAI, installing libraries, and using prompt templates to create flexible prompts; switch models easily, run chat models, and generate embeddings.