Learn Articles
Browse our learn articles on AI inference, model selection, and GPU planning.
What Are Embeddings? How Text Becomes Vectors of Meaning
Text embeddings turn words and documents into vectors so meaning can be measured by distance. How they are produced, what they power, and where they fall short.
What Is Tokenization? How Language Models Read Text
Before a model can read or write a single word, text is broken into tokens. How tokenization works, why subword units won, and why tokens decide both cost and context limits.
What Is Model Routing? Matching Every Request to the Right Model
Model routing directs each individual request to the most appropriate model from a pool, rather than sending all traffic to one model. Here is how routing decisions are made, the axes a router optimizes, and where it differs from load balancing and Mixture of Experts.
What Is a Context Window? How LLM Context Limits Work and Why the Headline Number Misleads
A context window is the slice of text a language model can read and generate inside a single request. This post explains how context windows actually work, why the advertised length and the usable length rarely match, and how to size a context budget for production workloads.
What Is Retrieval-Augmented Generation? How RAG Works and Why Most Production LLM Apps Use It
Retrieval-augmented generation (RAG) is the architecture that lets a language model answer questions from a corpus of documents the model has never seen. Here is how RAG works, the components of a production RAG system, the variants that have emerged since the original 2020 paper, and where RAG breaks in practice.
What Are Small Language Models? Where the Sub-10B Tier Earns Its Keep and Where It Breaks
Small language models are open-weight transformers in the roughly one to ten billion parameter range that approach frontier capability on narrow tasks at a fraction of the inference cost. Here is how the category emerged, which model families anchor it, and where SLMs do and do not replace a frontier model in production.
What Is Tool Calling? How LLMs Invoke External Functions and Why Agents Depend On It
Tool calling lets a language model emit structured function-call requests during a generation, which the application executes and feeds back as a new message. Here is how the protocol works, why provider implementations diverge, and what production teams need to monitor when agent loops run on top of it.
What Are Reasoning Models? How Test-Time Compute Works and Why It Costs More
Reasoning models like the OpenAI o-series, Claude with extended thinking, and DeepSeek R1 trade output tokens for accuracy on hard problems. Here is how test-time compute works, why these models cost ten to fifty times more per request, and when the economics actually justify their use.
AI Inference vs Training: The Technical and Economic Differences
Training and inference share the same underlying math, but diverge in hardware, software, cost structure, and ownership. Here is what actually separates the two phases and why the distinction matters for anyone building on top of foundation models.
What is AI Inference? A Complete Guide
AI inference is the runtime phase where a trained model produces outputs from new inputs. It is the layer that dominates the cost, latency, and reliability of every AI-powered product in production.
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