OpenAI API with Node.JS Bootcamp: ChatGPT API, GPT-4, DALL·E

Download and install Visual Studio Code from code.visualstudio.com, choose the stable build, and complete setup with optional tasks like desktop icon and context menu options.

Install Node.js and npm to run JavaScript on the server side, verify versions, and prepare your environment across Windows, macOS, and Linux for the course packages.

Set up a new project in Visual Studio Code, create a folder, open a terminal, initialize with npm init -y, and install the OpenAI package to create node_modules and package.json.

Set up your OpenAI account on platform.openai.com and generate a secret API key. Copy or revoke keys and note the $5 free credits for three months, with a paid upgrade.

Discover OpenAI models and how to list them via code using the OpenAI library, while noting newer conventions and model roles like GPT-3.5 turbo and whisper.

Overview of the main models: GPT-4, GPT-3.5 (turbo and text-davinci-003), DALL·E, and Whisper; with notes on fine-tuning and future image input for GPT-4.

Learn to interact with text-based models in the OpenAI playground, exploring chat mode and completion mode with system instructions and prompts, and how to write code to replicate these interactions.

Explore interacting with text-based OpenAI models via the chat endpoint and OpenAI API reference, using messages and a GPT-3.5 turbo model with streaming, and build a reusable query GPT function.

Explore how GPT-4 improves reasoning and memory with up to 8000 tokens, vs 4k for GPT-3.5 turbo, and learn GPT-4 vision to convert images to text using data URLs.

Explore streaming in the OpenAI API by using the stream parameter to receive chat completion deltas in real time, iterating with for await over chat completion chunks in Node.js.

Discover how temperature, top-p, frequency and presence penalties shape OpenAI text generation, and learn to tune maximum length, stop sequences, best-of, and inject start and restart text for controlled responses.

Learn to craft effective prompts for language models by defining tasks, providing context, and tuning temperature and penalties, using clear instructions and trial-and-error to guide outputs.

Explore prompt engineering fundamentals and techniques to craft effective prompts for GPT development. Apply trial-and-error, write clear instructions, and tune parameters like temperature and penalties for reliable chat results.

Explore using delimiters in prompt engineering to prevent prompt injection, separating user input from instructions, with Gmail-focused examples and notes on limitations and structured outputs.

Discover how to use structured outputs in prompt engineering, transforming GPT results into JSON formatted data and a JavaScript array of objects with name and distance in million kilometers.

Demonstrate structured outputs with an ai pizza ordering assistant that converts natural language orders into a json object, saves them in a local json file, and generates an itemized invoice.

Explore how few shot prompts, chain of thought prompting, zero shot code, and self-consistency boost GPT outputs by steering reasoning with exemplars and step by step prompts.

Explore whisper API, a hosted open-source speech-to-text model. Transcribe and translate multilingual audio with whisper one, using file, model, prompt, response format, and ISO language codes at 0.006 per minute.

Integrate the pizza delivery assistant with Whisper API to transcribe natural language orders into structured JSON, using manual and automatic recording workflows in Node.js.

Explore the text to speech api's create speech endpoint to generate audio from text, with model, input, voice, format, speed parameters, and mp3 default.

Discover how DALL-E converts text into images, edits images, and creates variations, including outpainting, using the DALL-E 2 model with 12 billion parameters and 1024 by 1024 resolution.

Interact with the Dali API to create images from prompts and learn how the image object returns base64 json data for easy saving.

Learn to craft precise DALL·E prompts using adjectives, comparisons, and context, then enhance results with lens and photographer references while staying concise.

Learn to edit images with the DALL·E API by supplying an image and a mask that marks editable regions (transparent areas indicate edits), then describe changes with a prompt.

Explore creating image variations with the dall-e API by uploading an input image, setting the number of outputs, and choosing the b64_json response format for immediate review.

Explore Dall-E 3 through ChatGPT Plus and hover over GPT-4 to access it. Compare its image generation with Dall-E 2 and anticipate the Dall-E API release.

Explore fine tuning by training a pre-trained model on task-specific data to boost performance. Prepare datasets in OpenAI format, upload data, create the fine tune model, and test it.

Prepare datasets for fine tuning by converting data to json or json lines, arranging it as system, user, and assistant conversations, and validating with a json lines validator.

Upload datasets to OpenAI for fine tuning by creating a file with OpenAI files create, streaming data, and obtaining a file id with processed status to prepare a fine-tuned model.

Create a fine tuning job with the OpenAI API by selecting GPT 3.5 turbo and supplying training file ID, then adjust hyper parameters like n epochs and add a suffix.

Learn how to deploy a fine-tuned model, monitor training loss, and adjust epochs. Apply the fine-tuned model in code or playground with system instructions for your use case.