Why LLM Evaluation Is Hard Evaluating a language model is fundamentally different from evaluating a traditional software system. A classifier has a ground-truth label for every input — you measure accuracy against it. An LLM can produce dozens of valid responses to the same prompt, making “correct” a genuinely ambiguous concept. How do you measure … Read more
What Are Embedding Models? An embedding model converts text — a word, sentence, paragraph, or entire document — into a dense vector of floating-point numbers. That vector is a point in a high-dimensional space, and the key property is that texts with similar meanings end up close together in that space, while texts with different … Read more
What Are AI Guardrails? AI guardrails are the controls you put around an LLM to constrain its behaviour — preventing harmful outputs, enforcing output formats, keeping the model on-topic, and ensuring compliance with your application’s policies. Without guardrails, even a well-prompted model will occasionally produce outputs that are incorrect, off-topic, policy-violating, or unsafe. Guardrails are … Read more
What Is a Context Window? The context window is the total amount of text — measured in tokens — that a language model can see and process at one time. Everything the model uses to generate a response must fit inside it: the system prompt, the conversation history, any documents you have injected, the user’s … Read more
What Is the Model Context Protocol? The Model Context Protocol (MCP) is an open standard introduced by Anthropic in late 2024 that defines how AI applications connect to external tools, data sources, and services. Before MCP, every team building an LLM application had to write custom integration code for each tool they wanted to expose … Read more
What Is Tool Calling? Tool calling — sometimes called function calling — is the mechanism that lets an LLM do more than generate text. Instead of producing only a prose response, a model can emit a structured signal indicating it wants to invoke an external function: search the web, query a database, run a calculation, … Read more
Introduction Remix is a full-stack React framework that emphasises web fundamentals — HTTP, forms, and the browser’s native capabilities — over client-side abstractions. Its loader and action model gives every route a clean server/client boundary, making it straightforward to put Ollama on the server side where it belongs and stream results to the browser. Remix’s … Read more
Introduction Deno is a modern JavaScript and TypeScript runtime built by the original creator of Node.js. It ships with TypeScript support out of the box, a secure-by-default permissions model, a built-in standard library, and a native HTTP server — all without needing a package.json or a separate build step. These qualities make Deno an attractive … Read more
Introduction Angular is a batteries-included frontend framework from Google that pairs a strong opinions on project structure with powerful built-in tools for dependency injection, reactive programming with RxJS, and HTTP communication. When you add Ollama to an Angular project, you get a productive local AI development stack — RxJS observables are a natural fit for … Read more
Introduction Next.js is the most widely used React framework for production web applications, offering server-side rendering, API routes, and a file-based routing system that makes it straightforward to build both the frontend and backend of an application in a single codebase. When you combine Next.js with Ollama, you can build AI-powered web applications that run … Read more
