Building Generative AI applications can feel daunting for traditional app developers. What does the end-to-end application development cycle look like? What models should I use, and where do I find them? What tools should I be using for build, test, and deploy, my AI application? This blog post gives you a sneak peek at a week-long series of posts that were just published, that give you a hands-on journey through the process. Let’s learn more!

Kicking Off Azure AI Week!

This week we published a 5-part blog on the Build Intelligent Apps initiative’s #30DaysOfIA series. Our focus was application developers who wanted to build a custom copilot code-first on Azure AI, allowing them to have more control over various decisions made in the end-to-end workflow for generative AI applications. We did this by walking through two core samples (Contoso Chat and Contoso Creative Writer) from prompt to production. Along the way, we shared insights into key tasks and the developer tools to simplify them.

In this blog post, we’ll briefly introduce the two applications and give you an overview of what the series covers, with links to each post for deeper dives. Ready? Let’s Go!

1. What are we building?

Our first application is Contoso Chat, a customer service chatbot that answers user questions about a retailer’s products,  using the Retrieval Augmented Generation pattern (RAG) to ground responses in both the product catalog and customer purchase history.
 

Our second application is Contoso Creative Writer, a content publishing assistant that uses the Multi-Agent Conversation pattern to coordinate and execute multiple tasks autonomously, on behalf of the user.

2. How are we building it?

The figure below shows the AI Application Architecture for the Contoso Chat retail copilot. User requests are received through an endpoint hosted in Azure Container Apps, then processed using a RAG-based workflow that uses Azure AI Search (product index) and Azure Cosmos DB (customer database) with Azure OpenAI Services (model deployment) to process user requests and return the response back to the UI.

The next figure shows the AI Application Architecture for the Contoso Creative Writer multi-agent copilot which follows a similar user interaction flow – except that processing now requires coordination across multiple agentic AI tasks before final output is generated.

3. What does the developer workflow look like?

We’re glad you asked! If you’ve explored generative AI application development before, you’re probably familiar with this GenAIOps application lifecycle which breaks down the developer workflow into 3 stages: ideation (build and validate a prototype), augmentation (iterate & evaluate with larger input datasets), operationalization (deploy to production).

In this blog series, we map this lifecycle to a very clear developer workflow as shown below, giving you an intuitive sense for the task to perform, and the tool to use to accomplish it, at each stage.

Get started reading the posts, in this order:

1. Kicking Off Azure AI Week – Learn about the app scenarios, architecture & lifecycle.
2. Provision with AZD – Provision Azure infrastructure & setup your dev environment.
3. Ideate with Prompty – Build an app prototype using Prompty assets and tooling.
4. Evaluate with AI – Build custom evaluators and use AI-assisted evaluation flows.
5. Deploy with ACA – Create a FastAPI server & deploy with Azure Container Apps.

Here's a visual summary of what you'll learn:

If you found this series valuable, please star the repos to help others discover them!

• Contoso Chat – custom retail copilot with Retrieval Augmented Generation
• Contoso Creative Writer – custom content copilot with Multi-Agent Collaboration

5. Next Steps

Want to get hands-on experience building these copilots? Take these actions today!

1. Register for Microsoft AI Tour - join an instructor-led workshop session.
2. Register for Microsoft Ignite - look for related lab & breakout sessions on Azure AI.
3. Browse the AI Templates Collection - explore samples for more scenarios.

Have a scenario you want to build a custom copilot for? Have questions about Prompty, Azure AI Studio, or the GenAI Ops workflow? Want to provide feedback on the samples? Leave us a comment here and let us know! Happy learning!