Summary and Next Steps

What have we covered?

Let’s quickly summarize what we’ve learned in this course.

We started our course with a quick refresher on Node.js and Express.js. We mainly looked at how promises and async-await work, explored the HTTP module in Node.js, and created our modules. We also learned how the GET and POST APIs, along with the query and path parameters, are implemented using Express.js. Finally, we finished the first chapter with an understanding of the middleware.

We then jumped into LLMs and discussed how they work and their use cases and then moved to OpenAI APIs, where we implemented text classification and sentiment analysis using the GPT models. Redis commands and practiced a lot of string, list, and set commands.

We built our first project, the Document Q&A Bot, using OpenAI and Langchain to implement the question-answer over the document. We first used RecursiveCharacterTextSplitter from LangChain to split the PDF document into smaller chunks and then created the vector embeddings using OpenAI from the PDF document. Finally, we gave a nice UI to interact with the application.

We then built our second  YouTube Video Captions Manager project, using OpenAI APIs and Express.js. We used OpenAI to convert audio to text using the whisper model and used Express.js to build our backend server and APIs to generate captions, summarize them, and rewrite them.

Then we learned how to connect our Redis data store with Node.js and created simple yet useful APIs implementing Redis operations using the redis npm package.

We then created our final project, the Twitter Tweet Generator project. We learned the importance of rate limiters and their use cases. We used Redis and implemented Redis operations to store the IP address along with the number of hits made in a window. We learned about Twitter APIs and how to use them to post tweets with images generated using OpenAI’s GPT and Dall•E models.

Conclusion 

We can build many cool new applications using what we’ve learned in this course. There are so many interesting AI applications coming up in the market daily. Think of a problem that can be automated or solved in less time and money. Some of the areas where a huge number of applications are coming up are:

• Content creation: AI can generate creative text formats, translate languages, and design graphics and videos. Similar to our Twitter tweet generation project, applications can be built to automate content creation for LinkedIn, Instagram, etc.

• Customer service: AI-powered chatbots can answer customer questions, resolve issues, and personalize interactions. Similar to our document Q&A project, explore how different types of data sources can be connected to OpenAI, and users can get answers from those data sources just by asking human-understandable questions.

• Data analysis: AI tools can analyze vast amounts of data to identify trends, predict outcomes, and generate insightful reports.

That’s all for this course. We hope you enjoyed it and learned many new concepts. We’d really love to hear your feedback, as it will help us improve the course and deliver richer content in the future. We appreciate your time!

Note: Don’t forget to take the final course assessment by clicking the “Next” button!