Complex Applications with the ChatGPT API: A Practical Short Course with Insights
Hello there! If you’ve ever wondered about building complex applications using large language models (LLMs), you’re in the right place. Aiming to delve deeper than merely prompting a model, we have developed a short course sharing best practices in creating intricate systems using an LLM. Our focus is on a comprehensive example that sets you on the path to developing an end-to-end customer service assistant.
Sequential Processing with an LLM
The customer service assistant model we're building invites the integration of multiple elements. You might begin with a user asking, "Tell me about what TVs are for sale?" As a developer, what steps would you take to deliver appropriate answers that fulfill your user's request?
Let's journey together through the process:
1.Evaluate the Input: The initial step involves a quick check to filter out any problematic content. For instance, you don’t want hateful speech finding its way into your friendly customer service assistant system.
2. Process and Classify the Query: Next up, your system will dive deeper into analyzing what the user is asking. Classify it as a complaint, a product information request, etc. This step forms the cornerstone for generating a pertinent response.
3. Fetch Relevant Information: After determining that the user needs product information, your system would then retrieve relevant data about TVs.
4. Generate a Response: Now comes the exciting part. Utilize your LLM to frame a helpful and amiable response based on the collected information.
5. Check the Output: Finally, one last checkpoint. Ensure that your answer doesn’t have any problematic content like inaccurate or inappropriate answers.
In a nutshell, despite the steps of the process being invisible to the user, a sophisticated application frequently requires several internal procedures. You'll often sequentially process the user input in multiple stages to reach the final output presented to the user.
## Continuous Improvement and Development
But it's not enough to just build a system. Over time, as you construct intricate structures employing LLMs, it's essential to keep enhancing the system through consistent development and testing.
With this course, we aim to furnish you with an insider's view of what developing an application supported by an LLM feels like. More importantly, we wish to arm you with best practices for evaluating and progressively improving your system.
## Expressing Gratitude
Nothing substantial comes into being in isolation. We would like to extend our heartfelt thanks to everyone involved in creating this short course. We're eternally grateful to our colleagues on the OpenAI side, especially Andrew Kondrich, Joe Palermo, Boris Power, and Ted Sanders. Likewise, the DeepLearning.AI team deserves accolades, particularly Geoff Ladwig, Eddy Shyu, and Tommy Nelson.
## Let's Get Started!
Through this short course, our aim is to leave you feeling confident about your capacity to build and maintain comprehensive, multi-step applications. We want to empower you to not just create, but to persistently improve systems with your newfound knowledge. So put on your developer's hat and let’s dive into the intricacies of building systems using the ChatGPT API!
