Artificial Intelligence

Retrieval-augmented generation (RAG) is easy to demo and surprisingly hard to evaluate. A fluent answer can hide weak retrieval, while a good retriever can be blamed for an answer model that ignores its evidence. A useful evaluation process separates those failure modes. You do not need thousands of examples to begin. A carefully maintained golden dataset of 30 to 100 representative questions can catch many regressions before users do. Define what the system is supposed to do Start with the product contract rather than a model metric. For a documentation assistant, useful requirements might be:

Large language models are useful when software needs to turn ambiguous text into a structured decision, extraction, or plan. The dangerous shortcut is to treat a model response as if it were already trusted application data. Even when a provider can constrain output to JSON or a schema, the result can still be semantically wrong: a date can be impossible, an identifier can refer to a nonexistent record, or a supposedly positive amount can be negative. Reliable integrations therefore need a contract boundary between model output and the rest of the system.

Have you ever wanted to run a large language model (LLM) directly on your own computer without depending on a cloud service? Tools such as Ollama make it possible to manage and run language models locally from a laptop or desktop. This article walks through the basic setup and shows how to run a model on Linux and macOS. Installing Ollama on Linux For Linux, the installation process is straightforward:

In this article, we will experiment with YOLOv8 for object detection in images. YOLO (You Only Look Once) is one of the best-known object detection algorithms. YOLOv8 offers fast performance and accurate results. Fortunately, the ultralytics library makes it straightforward to use. What You Need Before you begin, make sure you have: Python The ultralytics library If it is not installed yet, install it with pip: Copy pip install ultralytics Simple Code Example Here is a short example that detects objects with YOLOv8:

In this article, we will explore SMS spam detection with BERT. BERT (Bidirectional Encoder Representations from Transformers) is a widely used NLP model architecture that can be adapted to many text tasks, including classification. PyTorch and Hugging Face Transformers provide a convenient way to work with BERT models. What You Need Before you begin, make sure you have: Python PyTorch Hugging Face Transformers An SMS dataset, such as the SMS Spam Collection, if you plan to fine-tune a classifier Install the required libraries with:

YOLO (You Only Look Once) is one of the most popular object detection approaches. In this article, we will use YOLOv8 with OpenCV to perform real-time object detection through a webcam. Prerequisites Make sure you have: Python 3.8+ The ultralytics library for YOLOv8 The opencv-python library for webcam access Install the dependencies with pip: Copy pip install ultralytics opencv-python Example Code The following Python example performs object detection from a webcam stream:

In this article, we will use YOLOv8 to detect objects in an image and print each object’s label and confidence score. YOLO (You Only Look Once) has long been a popular choice for object detection, and YOLOv8 provides a convenient Python API through the ultralytics package. Here is how to use it. Prerequisites Make sure Python and the ultralytics library are installed on your system. If needed, install the package with pip: