LLM Response Caching: How to Cut API Costs and Latency with Exact, Semantic, and Prompt Caching

Why Caching Matters for LLM Applications LLM API calls are expensive and slow compared to almost every other operation in a software stack. A single call to a frontier model can cost between a fraction of a cent and several cents, take 1–5 seconds to complete, and involve significant computational overhead on the provider’s side. … Read more

Chain-of-Thought Prompting: How It Works, When to Use It, and Advanced Variants

What Is Chain-of-Thought Prompting? Chain-of-thought (CoT) prompting is a technique that instructs a language model to show its reasoning step by step before producing a final answer. Rather than jumping directly to a conclusion, the model works through the problem explicitly — identifying relevant information, applying logic, considering intermediate results — and only then commits … Read more

LLM Evaluation Frameworks: How to Measure What Your Model Actually Does in Production

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

Embedding Models Explained: How They Work, Key Models in 2026, and How to Choose One

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

AI Guardrails: How to Add Input Validation, Output Controls, and Safety to LLM Applications

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

Model Context Protocol (MCP) Explained: What It Is and How to Build Servers and Clients

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

How to Use Ollama with Remix

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

How to Use Ollama with Deno

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