The Complete 'No-Code' ChatGPT & OpenAI API Course

Explore prompt engineering by writing natural language prompts to train and fine-tune models like ChatGPT. Learn the four building blocks: task, input, output, and context, and apply them in projects.

Explore zero-shot prompt engineering to craft clear, task-focused prompts for ChatGPT and other pre-trained models, understanding inputs, outputs, and context to maximize accuracy and usefulness.

Explore role prompt engineering by guiding GPT to respond in specific personas like Darth Vader or Dickens, while analyzing task, input, output, and context for coherent, testable prompts.

Learn few-shot prompt engineering, using a few examples to guide model responses in chat, boosting coherence and precision for sentiment labeling and structured outputs.

Master chain prompt engineering by building a sequence of prompts that refine outputs for long-form text and code, using role-based, prompting, and few-shot techniques to guide coherent, accurate results.

Advance your prompt engineering skills beyond ChatGPT to other large language models. Engage in hands-on, project-based sections with OpenAI and GPT, guided by course resources.

Explore building a no-code reply generator that automatically posts haiku replies to tweets using Zapier, Twitter, Google Sheets, and the OpenAI ChatGPT API.

Create a Google sheet named generate replies to store tweets and haikus generated via the ChatGPT API, with two sheets: tweets (tweet, link) and haikus (tweet, link, haiku).

Build a two-step zap with Zapier to map Twitter data to Google Sheets, triggering on user tweets and recording tweet text and link for automation.

Connect Google Sheets to the ChatGPT API via a three-step Zap to transform tweets into haiku poems and write the results back to Google Sheets.

Automate Twitter replies with a no-code workflow by connecting Google Sheets to Twitter via Zapier, posting haiku poems generated from new spreadsheet rows in response to tweets.

Explore how a no-code Zapier workflow uses Google Sheets as a simple database, calls the ChatGPT API to turn tweets into haikus, and tweets them automatically.

Conclude this section by showing how to build an AI powered automation tool without coding, transforming tweets into haiku poems using Zapier and the OpenAI API.

Build a telegram bot that uses the OpenAI ChatGPT API to generate personal, conversational replies from pasted text, all connected via make with no code.

Create a telegram bot with botfather by naming it and choosing a username ending in bot, then generate and save its api token for later use with make.

Connect Telegram to the ChatGPT API using Make, set up a Telegram bot and webhook, and configure OpenAI API keys to enable chat completions with GPT-3.5 Turbo.

Connect ChatGPT to Telegram by adding a Telegram bot module that sends OpenAI API output as a text message to a specific chat using the Chat ID.

Set up a Make automation that retrieves messages from Telegram, feeds them to the GPT API, and sends responses back; enable scheduling and review scenario settings, diagram, and history.

Celebrate building a smart Telegram bot with the ChatGPT API that crafts coherent replies to texts and emails, and explore ideas for translation, summarization, and knowledge-based bots.

Build a no-code stock sentiment analyzer for Google headlines, classifying news as positive, neutral, or negative to gauge market perception, using Airtable, Parabola, and News API.

Create an Airtable database to store article data—title, description, author, publication, title URL, and sentiment—and customize the base and table names for later Parabola integration.

Connect the News API in Parabola to pull top headlines about GOOG, configure a flow with GET requests, and parse articles via response.json for stock sentiment data.

Learn to extract data with ai in parabola, tidy api sources, and create a publication column by linking steps with prompts and ai functions, without needing openai api keys.

Learn to categorize data with AI by adding a sentiment label (positive, neutral, negative) using the categorize function, feeding title and description, and refining a prompt for stock analysis.

Tidy and restructure data in parabola by removing unused columns and reordering the rest. Apply lowercase casing and explore transforms to manipulate data without coding.

Export data from Parabola to Airtable by authorizing the connection, selecting create records, and mapping title, description, author, publication, URL, and sentiment to the Alphabet table.

Take a bird's-eye view of the project to see Parabola's integrations, transforms, and AI options, and follow the flow from API data pull to exporting to Airtable.

Recap how the no-code parabolic tool analyzes news sentiment with AI to inform stock trading, highlighting Google vs. Microsoft OpenAI dynamics.

Create a review replier in Google Sheets using the ChatGPT API, starting with a no-code approach and moving to low-code JavaScript to generate replies for course reviews.

Create a Google Sheet, populate it with sample course reviews, and implement a script inside the sheet to auto-generate replies using the GPT API.

Master prompting ChatGPT to generate code with detailed prompts and prompt engineering, creating a JavaScript function for Google Sheets and exploring how to structure API interactions for future OpenAI use.

Learn to add a custom Google Sheets script with Apps Script to generate ChatGPT replies from reviews using the OpenAI API.

Discover how a short script powers scalable content generation and email replies via the OpenAI API and ChatGPT, with practical Google Sheets data automation.

Wraps up a no-code project that automates replying to reviews in google sheets using the openai api directly, highlights tokens and temperature, and encourages applying these skills to future workflows.

Complete the course and build on an evergreen foundation for using OpenAI tools, including ChatGPT and the OpenAI API, while practicing no-code AI projects.

Learn why ChatGPT is at capacity and apply five fixes, including avoiding peak times, refreshing the browser, using a VPN, incognito mode, and checking the status page.

Discover how ChatGPT uses deep learning and neural networks to analyze text and generate human-like responses. Learn to spot inaccuracies and biases and apply critical review.

Explore how OpenAI treats tokens as units of text, including words, punctuation, and symbols, and see how token patterns guide training of models like ChatGPT, next-word predictions, and API usage.

Explore four ethical concerns in ai, including bias, privacy, employment impact, and accountability, and examine data handling practices and the need for oversight in tools like ChatGPT and OpenAI.

Explore how temperature in OpenAI models controls output randomness to balance accuracy and creativity. Low temperatures yield predictable outputs; high temperatures foster creativity. Experiment with values to see different results.

OpenAI's 2015 birth introduced founders Elon Musk, Sam Altman, and Greg Brockman. AGI mission emphasizes benefit to humanity, long-term safety, cooperative orientation, and technical leadership, with ChatGPT as a showcase.

ChatGPT does not have direct internet access and relies on data up to 2021. However, plugins and integrations allow it to reach internet-connected systems and apps for up-to-date information.

Explore ChatGPT pricing, including free access, ChatGPT Plus at $20 per month, and OpenAI API pricing based on tokens; refer to the official OpenAI pricing page for the latest details.

ChatGPT learns through training on massive text data, recognizing patterns and refining parameters with curated datasets. It then fine-tunes for domain tasks using user data.

Assess ChatGPT reliability by recognizing hallucinations and confirming information, since it’s a machine learning model, not a human. Structured prompts improve accuracy; longer prompts may reduce precision in critical domains.