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💻   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.


💻   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"


💻   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.


📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).


📱   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

🗣   Captions (English and Spanish): Click on the gear icon (⚙) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

🔅   Video Quality: If you do not have access to high-speed internet, click on the gear icon (⚙) on the video and then from Quality, choose the quality that works the best for your Internet speed.

🖥   Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

√   Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.


🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

📅   Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

☕   Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

💬   Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

✍   Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.


📚   Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. 👇

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🙂   Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

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Welcome to this course on building systems using the ChatGPT API. Previously, Isa and I had presented a course on how to prompt ChatGPT, but a system requires much more than a single prompt or a single call to an LLM or large language model. In this short course, we'd like to share with you best practices for building a complex application using an LLM. We used the running example of building an end-to-end customer service assistant system that chains multiple calls to a language model, using different instructions depending on the output of the previous call, and sometimes even looking things up from external sources. So for example, given a user input like, "Tell me about what TVs are for sale?", we'd use the following steps to process this. First, you can evaluate the input to make sure it doesn't contain any problematic content, such as hateful speech. Next, the system will process the input. It will identify what type of query this is. Is it a complaint or a product information request and so on? Once it has established that it is a product inquiry, it will retrieve the relevant information about TVs and use a language model to write a helpful response. Finally, you'll check the output to make sure it isn't problematic, such as inaccurate or inappropriate answers. One theme you see throughout this course is that an application often needs multiple internal steps that are invisible to the end-user. You often want to sequentially process the user input in multiple steps to get to the final output that is then shown to the user. And as you build complex systems using LLMs, over the long term you often want to also keep on improving the system. So I'll also share with you what the process of developing an LLM-based application feels like, and some best practices for evaluating and improving a system over time. We're grateful to many people that have contributed to this short course. On the OpenAI side, we're grateful to Andrew Kondrich, Joe Palermo, Boris Power, and Ted Sanders. And from the DeepLearning.AI team, thank you also to Geoff Ladwig, Eddy Shyu, and Tommy Nelson. Through this short course we hope you'll come away confident in your abilities to build a complex multi-step application and also be set up to maintain and keep on improving it. Let's dive in!
course detail
Next Lesson
Building Systems with the ChatGPT API
  • Introduction
    Video
    ・
    2 mins
  • Language Models, the Chat Format and Tokens
    Video with Code Example
    ・
    19 mins
  • Classification
    Video with Code Example
    ・
    4 mins
  • Moderation
    Video with Code Example
    ・
    8 mins
  • Chain of Thought Reasoning
    Video with Code Example
    ・
    10 mins
  • Chaining Prompts
    Video with Code Example
    ・
    19 mins
  • Check Outputs
    Video with Code Example
    ・
    6 mins
  • Evaluation
    Video with Code Example
    ・
    5 mins
  • Evaluation Part I
    Video with Code Example
    ・
    19 mins
  • Evaluation Part II
    Video with Code Example
    ・
    9 mins
  • Summary
    Video
    ・
    1 min
  • Course Feedback
  • Community