Understand the development lifecycle of a large language model (LLM) app

Before understanding how to work with prompt flow, let’s explore the development lifecycle of a Large Language Model (LLM) application.

The lifecycle consists of the following stages:

1. Initialization: Define the use case and design the solution.
2. Experimentation: Develop a flow and test with a small dataset.
3. Evaluation and refinement: Assess the flow with a larger dataset.
4. Production: Deploy and monitor the flow and application.

During both evaluation and refinement, and production, you might find that your solution needs to be improved. You can revert back to experimentation during which you develop your flow continuously, until you’re satisfied with the results.

Let’s explore each of these phases in more detail.

Initialization

Imagine you want to design and develop an LLM application to classify news articles. Before you start creating anything, you need to define what categories you want as output. You need to understand what a typical news article looks like, how you present the article as input to your application, and how the application generates the desired output.

In other words, during initialization you:

1. Define the objective
2. Collect a sample dataset
3. Build a basic prompt
4. Design the flow

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