Mastering ChatGPT & OpenAI APIs: A Concise Guide

Set up your Python development environment by installing Python from python.org, creating and activating a virtual environment, and installing dependencies with a requirements.txt file for this course.

Set up your OpenAI account, generate and securely store your API key, configure it as an environment variable in zshrc, verify access, and make your first request to GPT models.

Set up your OpenAI key and launch a Jupyter notebook to run first API requests, exploring the completion and chat completion endpoints in an interactive coding environment.

Access the completion endpoint using OpenAI dot completion dot create, provide a prompt, choose the model text DaVinci three, set temperature and max tokens, and read the json choices.

Learn to use the chat completion endpoint for multi-turn conversations by sending a messages array with roles and content, parsing assistant responses from choices[0].message.content.

Examine the OpenAI API response structure, including id, object, created, model, choices, and usage, and understand text and chat completion content and finish reason.

Understand tokens as the fundamental text units used by chatbots like ChatGPT, how input and generated text count toward token totals, and how tokenization tools and encodings reveal token structures.

Build your own ChatGPT using OpenAI APIs by implementing chat completion with a messages array, maintaining conversation history, and enabling multi-turn conversations through code in a loop.

Master prompt engineering by crafting clear, specific instructions, using delimiters to separate input sections, and prompting for structured outputs, with code examples for developers building ChatGPT apps.

Illustrates prompting an OpenAI model to summarize a news article with clear instructions, using angled brackets for delimitation and defining output length.

Explore prompting to produce structured outputs in specific formats, such as json or xml, enabling reliable parsing for applications like a book recommendation system.

Learn zero-shot prompting and few-shot prompting through example-driven tasks, such as classifying tweets as positive, negative, or neutral, using the chat completion endpoint.

Adapt a pre-trained model with fine tuning to task-specific needs, delivering higher quality results and token savings. Build a multi-class tweet sentiment classifier by training with OpenAI's fine tuning API.

Prepare a fine-tuning dataset by converting tweets to prompt-completion pairs, adding a prompt separator, mapping sentiments to numbers, and splitting data into train, validation, and test sets saved as jsonl.

Train a fine-tuned OpenAI model via the CLI using train and validation files for a three-class classification with a base ada model.

Fetch and analyze validation results from the fine-tuning csv to track training loss across steps, monitor batch size 16, and assess accuracy and weighted F1 score around 88%.

Evaluate a fine tuned model on test data using OpenAI completions API, with batch prompts, and assess accuracy and classification metrics, setting temperature to 0 and max tokens to 1.

Explore embeddings as dense vector representations that convert diverse data, including text, images, audio, video, databases, and graphs, into numeric vectors via pre-trained networks like GPT 3 or 4.

Learn how embeddings represent text as vectors and capture similarity. Convert documents and queries into embeddings to locate the closest match in a text corpus.

We explore the OpenAI embedding API by creating an embedding with a text embedding model, inspecting the response data, and extracting the final embedding from the data key.

Learn how vector databases store embeddings from an embedding model and enable nearest-neighbor retrieval for text search; queries are converted into embeddings and results returned by Pinecone, Milvus, and Vivit.

Create a vector index with OpenAI embeddings and fies by Facebook AI, convert lists to numpy arrays, build a 1536‑dim inner product index with ADA, and query top 3 similarities.

Learn to master Pinecone, a popular vector database, from account creation and api keys to creating an index, embeddings, and metadata-based queries.

Build a document querying system by converting text to embeddings, splitting into 100 word chunks with 10 word overlap, indexing with cosine similarity, and answering questions with ChatGPT from context.

Learn how to use OpenAI's image endpoint with DALL-E to generate and vary images, including prompts, image size, and handling PNG inputs for variations.