Creating LLM Workflows with Prompt Flow
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LLMs, or large language models, are powerful tools that can generate natural language texts for various purposes and domains. However, developing LLM-based applications can be challenging, especially when you need to integrate multiple sources of information, such as external APIs or other LLM prompts. That’s why Microsoft has introduced Prompt Flow, a new suite of tools that simplifies the end-to-end development cycle of LLM-based applications.
What is Prompt Flow?
Prompt Flow is a framework that allows you to create, test, and deploy LLM-based applications using a visual interface. You can use Prompt Flow to design complex workflows that combine multiple LLM prompts and external data sources.
An example that I recently worked on is a chatbot that can help employee with HR use-cases. To accomplish this, I needed to make connections to other tools that contained employee information, policies and vacation days.
Prompt Flow is based on the concept of Retrieval Augmented Generation (RAG), which is a technique that enhances the generation capabilities of LLMs by incorporating external knowledge. RAG allows you to insert data into a prompt that can then be used to reason on.
Why Use Prompt Flow?
Using Prompt Flow comes with a few benefits such as:
- Ease of use: Its drag-and-drop interface allows you to create and edit workflows, without writing any code. There are a couple of pre-built templates that should really help you get on your way.
- Flexibility: You can customize your workflows by adding, removing, or modifying any component. You choose the models that you want to use, any model deployed in your Azure OpenAI Studio is available.
- Scalability: You can deploy your workflows with one click. Behind the scenes, these REST API endpoints run on the scalable Azure infrastructure that you are used to.
- Collaboration: Your workflows are available to other users in your tenant. You can collaborate on workflows, experts of certain APIs can take…
