In Part 1, we built the Backend, a serverless extraction pipeline that turns receipt images into data.
In this Part 2, we will build the user-facing Frontend: a .NET 8 Web App that allows users to securely log in with their Microsoft account and upload receipts.
In this post, I’m going to walk you through building a Serverless Receipt Processing Application on Azure. Understand the architecture how the components work together from backend to frontend.
Did you know you can publish your website on Azure and take advantage of Azure's global content distribution for free? Additionally, you can automate your content publishing with GitHub CI/CD at no cost.
In this post, I will explain how to host your website on Azure Static WebApp using GitHub CI/CD. It really is simple.
RAG has become a common term in modern AI discussions but what does it actually mean?
RAG stands for Retrieval Augmented Generation. In simple terms, it is a technique that allows large language models (LLMs) to generate answers using external knowledge, rather than relying only on what was learned during model training.
Let’s make this easier to understand with a concrete example.
Did you know that large language models don’t actually remember your conversations?
Every time you chat with a LLM, it feels continuous and stateful but under the hood, each response is generated from scratch. Any “memory” you experience is carefully reconstructed by the application.
In this post, I’ll walk you through building a PowerShell chat completion using Microsoft Foundry, and we’ll unpack exactly how chat history works, where it’s stored, and why the LLM remains fundamentally stateless.