Introduction

Coding a chatbot is a foundational step in LLM application development, but with the abundance of quick prototype solutions (e.g., using frameworks like Streamlit), there's a need to move toward more practical, scalable applications. To reach a wider audience, it's key to focus on platforms with large user bases like Discord or Telegram, which offer solid API integrations for easy deployment.

That said, coding LLM-based apps using APIs like OpenAI comes with its own set of challenges, primarily due to the inherent unreliability of these APIs.

OpenAI offers guidelines to enhance model reliability, with actionable advice in their documentation and example notebooks.

Setting the temperature to 0 is a frequently used approach to reduce output variability and ensure consistency. However, it doesn't necessarily solve issues around system reliability or build trust in the model's overall performance.

To tackle ambiguity, you need to apply coding experience, including well-defined prompt design, structured validation, and robust error handling. These practices together help improve the predictability and reliability of the app.

An effective strategy in prompt engineering is using a few examples in the prompt to guide the model's response. This method helps the LLM generalize, but it's important to note that identical inputs still might not produce identical results due to the model’s stochastic nature.

Now, let’s take a look at some code. Keep in mind that these examples are always evolving as the API improves and new patterns emerge.

And last but not least, most of my coding knowledge comes from trial and error, as well as reverse engineering. I don’t always fully apply coding practices, but that’s all part of my learning process.

As for now, enjoy...