Progress Worth Making.

Intro In the previous step we made the application a little more useful by introducing a very simple form of retrieval. Instead of sending every document to the LLM, we split the documents into chunks and tried to find the ones that looked relevant to the question. But there is an obvious problem with that approach which is we were essentially just counting words. That works when the question and the document use the same terminology, but it breaks very easily when the same idea is expressed differently. Imagine that one of our documents says:Authentication uses JWT tokens. And the user asks:How do users log into the system? This is where embeddings become useful. There isn’t much overlap between the above two sentences if we look at the actual words. A simple keyword search could easily miss the relevant document even though a human would immediately understand that they are talking about the same thing. This is where embeddings become useful. As usual, here is the code for what we are about to do: https://github.com/genoiucosmin/AIApp1/tree/Step3 Moving from words to meaning An embedding is a numerical representation of text. Instead of treating a sentence simply as a collection of words, an…

Introduction In Step 1, we built a simple grounded LLM application. The application could answer questions using our documents, but there was a fundamental problem: we sent all documents to the model for every question. That works for a handful of documents. It doesn’t work for a real knowledge base. So the next question is: How do we give the LLM only the information it actually needs? This leads us to retrieval and the moment we learn about RAG. As usual, you can find the code for this example here: https://github.com/genoiucosmin/AIApp1/tree/Step2 From all documents to relevant documents Step 1 Step 2 Let’s explain why this is cheaper, faster and more scalable conceptually. Chunking and our first retrieval mechanism In the previous example we sent in all documents to the LLM. This is clearly not something scalable, so we need to define a way of sending only the relevant data to the LLM. This is actually RAG – and we will implement a very primitive version of it, by just splitting the documents we had into chunks of 300 characters. Then we find out how many times each word from our question appears in each chunk. We then select the top…

With all the hype around AI, I was thinking it is a good time to actually build something simple, that will explain the basic principles of AI systems and LLMs. There are so many notions out there, so many new things, and technology is advancing so rapidly that even senior engineers have a rough time staying up to date. In order to make this as simple as possible, I will begin with the simplest example of how to integrate an AI API into a .Net app, and then build on that adding more features that will make clear a lot of concepts like : RAG, embeddings, grounding, chunking, guardrails, .Net agents and so on. I will build this in a public github repository, each step on a particular branch. Step 1 – From an LLM Call to a Grounded Application Note, before you try to run the code below, make sure you go over to https://platform.openai.com/ and generate an API key. Before running the application, create an OpenAI API key and configure it using .NET User Secrets or another secure configuration mechanism. You can test with your key in the appSettings.json also but never commit API keys to source control….

If you’ve ever been in a meeting where someone casually said “we just need to add RAG,” and you nodded while secretly searching what that meant under the table, this post is for you. The AI space moves so fast that even experienced developers can suddenly feel surrounded by unfamiliar buzzwords overnight. Terms like temperature, tokens, context window, embeddings, fine-tuning, and RAG get thrown around constantly, often without much explanation. This post is meant to build that foundation. I’ll walk through the core concepts and definitions you need to understand modern AI development. Don’t just skim the terms — try to understand the idea behind each one. Treat this post as a reference you can revisit from time to time, so the next time your company or team discusses a new AI strategy, you’ll understand what’s actually being talked about. You Are Not Behind. But You Do Need to Learn Some New Words. I have been writing code for a long time. Long enough to remember when JSON format was the future, when microservices were going to solve everything, when containers were the thing everyone had to learn or get left behind. Every few years something arrives that feels like…