AI

Understanding AI can feel overwhelming with all the jargon, but it doesn’t have to be! Here’s a beginner-friendly guide to some of the most commonly used AI terms, each with a practical example: 1. Artificial Intelligence (AI) AI is a broad field of computer science focused on creating systems that can perform tasks requiring human-like intelligence, such as recognizing images, understanding speech, making decisions, and translating languages. Example: A virtual assistant like Siri or Alexa that can answer questions and control smart devices. 2. Machine Learning (ML) A subset of AI where computers learn patterns and make decisions from data. Unlike traditional programming, ML models improve their performance as they process more data. ...

My Story: Why I Built a Local RAG System A few months ago, I found myself frustrated with the limitations and privacy concerns of cloud-based AI tools. I wanted to experiment with Retrieval-Augmented Generation (RAG) on my own terms—locally, with full control over my data and the ability to tinker under the hood. As a developer who loves open source, C#, and learning by doing, I decided to build my own local RAG system from scratch. ...

When building a Retrieval-Augmented Generation (RAG) application, selecting the right embedding model is crucial. After researching various models, I’ve summarized the key differences and use cases for two popular options: nomic-embed-text and all-minilm. Let’s dive in! Key Differences Between Nomic-embed-text and All-MiniLM 1. Architecture Nomic-embed-text: Optimized for handling large token context windows, making it suitable for both short and long text embeddings. All-MiniLM: Based on the MiniLM architecture, designed for sentence-level embeddings using self-supervised contrastive learning. 2. Performance Nomic-embed-text: Excels in semantic similarity tasks and produces high-quality embeddings for detailed documents. All-MiniLM: Offers faster inference speeds and is lightweight, making it ideal for real-time applications. 3. Use Cases Nomic-embed-text: Versatile and handles diverse text lengths, making it suitable for tasks like semantic search and clustering. All-MiniLM: Best for sentence-level tasks, such as paraphrase detection and short text similarity. Nomic-embed-text Use Cases Since nomic-embed-text is optimized for long text inputs and broad context windows, it’s ideal for applications requiring deep contextual understanding: ...

I’ve been exploring ways to integrate Retrieval-Augmented Generation (RAG) into my website, and I wanted to design a small, event-driven app to learn more about it. My goal was to create something simple yet practical for my site. Below is a Mermaid diagram I created to model the flow of my application. It highlights the main components of an event-driven RAG system, covering content updates, user queries, and background indexing: ...

Quantization has been on my mind lately as I explore ways to optimize my RAG (Retrieval-Augmented Generation) application. With so many options available, I wanted to break down the main techniques and share my thoughts on their strengths, trade-offs, and where they might fit best. Let’s dive in! 1. Scalar Quantization What It Is: Scalar quantization simplifies things by treating each component of a vector independently. For example, a 32-bit floating-point value can be mapped down to an 8-bit integer using a defined range and step-width. ...

When I first started exploring AI, I was eager to use my own data with large language models (LLMs). However, I faced a dilemma: should I fine-tune a model with my data or use Retrieval-Augmented Generation (RAG)? After diving into research, I discovered the strengths and challenges of each approach. Here’s what I learned: Fine-Tuning Fine-tuning involves retraining a pre-trained model on a specific dataset to adapt it to a particular domain or task. For example: ...

I have been working on my RAG application and when it comes to using my data (although small), I was thinking about performance and size. I decided to check Quantization. Quantization can be a useful technique in my RAG (Retrieval-Augmented Generation) workflow, especially when dealing with high-dimensional embeddings. It essentially reduces the precision of embeddings—compressing them so that the memory footprint is lower and the similarity searches can be faster, all while preserving most of the semantic information. Let’s break down the concept and how I might integrate it into the app: ...

Embedding models are a cornerstone of modern AI, transforming complex data—like words, sentences, or images—into numerical representations called embeddings. These embeddings are vectors in a multi-dimensional space, enabling machines to understand relationships between pieces of data. Here’s how they’re used across various fields: Applications of Embedding Models Natural Language Processing (NLP): Embeddings encode the meaning of words or sentences, powering tasks like sentiment analysis, machine translation, and question answering. Recommendation Systems: By embedding user preferences and item characteristics, these models enhance recommendations based on similarities. Image Recognition: Image embeddings identify objects or group similar images, making them essential for tasks like facial recognition. Search Engines: Embeddings improve search accuracy by finding data with similar representations. Clustering and Classification: They help identify patterns and group data efficiently, aiding in tasks like customer segmentation. How Embedding Models Work At their core, embedding models convert complex data into a format that computers can process and make decisions on. These models differ in several key aspects: ...

As part of a small AI project, I wanted to dive deeper into Retrieval-Augmented Generation (RAG) to understand its potential. Below is a summary of what I learned and why I chose to use it for my website. What is RAG? RAG stands for Retrieval-Augmented Generation. It’s a method used in AI to enhance the way large language models generate responses by incorporating external information. Here’s how it works in simple terms: ...

How I integrated Retrieval-Augmented Generation (RAG) functionality into my static Hugo site using C# and local LLMs to create a dynamic question-answering system.

I wanted to learn about training a model to see if I can train my own little model for my own needs. The Complete Guide to Training Machine Learning Models Training a machine learning model involves teaching it to make predictions or decisions by learning from data. Here’s a simple explanation of the process: 1. Data Collection and Preparation Gather relevant data from various sources Clean the data by handling missing values and outliers Normalize or standardize features to ensure equal importance Split the data into training, validation, and test sets (typically 70-15-15 or 80-10-10) 2. Feature Engineering Select relevant features that contribute to predictions Create new features from existing ones to improve model performance Reduce dimensionality if necessary to prevent overfitting 3. Choose a Model Select an appropriate algorithm based on your problem and data Linear models (e.g., linear regression, logistic regression) for simpler problems Tree-based models (e.g., random forests, gradient boosting) for complex data with non-linear relationships Neural networks (e.g., transformers or CNNs) for more complex tasks. 4. Train the Model Feed the training data into the model and adjust its parameters to minimize errors. The process typically involves: ...

I started to study for AI102 Certification. Preparing for the AI-102 certification is an excellent way to deepen understanding of Azure AI services. Here’s a breakdown of the key services that is related with exam: Azure AI Services Overview Azure Cognitive Services – These are pre-built AI models that allow developers to integrate AI capabilities into applications. They include: Vision (e.g., Computer Vision, Face API) Speech (e.g., Speech-to-Text, Text-to-Speech, Translator) Language (e.g., Text Analytics, Language Understanding) Decision (e.g., Personalizer) Search (e.g., Azure AI Search) Azure Machine Learning – A comprehensive cloud-based service that helps in building, training, and deploying ML models efficiently. Key components include: ...

Now studying more on Azure AI services, below is an in-depth look at how to map specific business requirements and use cases to the various Azure AI services. While the ultimate choice always depends on end-to-end needs—ranging from time-to-market and customization to scalability and regulatory constraints—here are some guiding principles: 1. Azure Cognitive Services When It Fits: Rapid Integration & Out-of-the-Box Capabilities: If your business needs to add AI capabilities quickly without developing and training custom models from scratch, Cognitive Services are ideal. They provide pre-built APIs for vision, speech, language, decision support, and more. Example use cases: ...

Let’s take a deeper dive into how you might choose between Azure Cognitive Services, Azure Machine Learning, and Azure Bot Services in real-world business scenarios. 1. Azure Cognitive Services When to Use It Quick Integration & Out-of-the-Box Capabilities: If your business needs an immediate boost by incorporating AI into existing applications without building models from scratch, Cognitive Services fit the bill perfectly. They offer pre-trained APIs for vision (image recognition, OCR), speech (speech-to-text, text-to-speech), language (sentiment analysis, translation), and decision making (personalizers, anomaly detectors). ...

Below is a detailed, step‐by‐step integration guide written entirely in C#. In this scenario, a retail chatbot uses: Azure Cognitive Services (LUIS): to parse customer messages and extract intents and entities. Azure Machine Learning: to call a custom recommendation model that produces personalized product suggestions. Azure Bot Services:—using the Bot Builder SDK for .NET—to drive a conversational interface that ties it all together. Step 1: Define Business Requirements & Architecture Business Use Case: A retail business wants a conversational assistant on its website. When a customer sends a query (e.g., “I’m looking for summer dresses”), the solution should: ...