Explainers
Evergreen explainers and reference guides, continuously maintained.
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AI Benchmarks Explained: What the Scores Actually Measure
Benchmark numbers drive coverage, procurement and valuations. Most of the widely-quoted ones are saturated, contaminated, or not comparable between models.
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Tool Use and MCP: How AI Connects to the Real World
Tool use is how a model stops merely talking and starts doing. MCP is the standard that stopped every integration from being…
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Why AI Models Hallucinate
Hallucination is not a bug in language models. It is a predictable consequence of how they are trained and, crucially, of how…
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Fine-Tuning vs RAG vs Prompting: Choosing the Right Approach
Three ways to make a model do what you want, routinely confused with each other. The deciding question is whether your problem…
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Reasoning Models Explained: Chain-of-Thought and Test-Time Compute
Reasoning models spend extra computation thinking before answering. The gains on hard problems are real, the costs are substantial, and the tradeoffs…
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RAG Explained: Giving AI Access to Knowledge It Was Never Trained On
Retrieval-augmented generation looks up relevant information and hands it to the model at question time. Large context windows were supposed to kill…
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AI Agents Explained: What They Are and What They Can Actually Do
An agent is a model given tools and a goal, running in a loop until the job is done. The honest picture…
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Context Windows Explained: Why AI Forgets, and What Changed
A context window is the model’s entire working memory. Understanding its limits explains most practical AI failures, and why a bigger window…
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Tokens and Embeddings: How Machines Represent Meaning
Models do not read words or understand meaning directly. They read tokens and operate on vectors. This distinction explains a whole class…
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Transformers Explained: The Architecture Behind Modern AI
Attention lets a model weigh every word against every other word at once. That one idea is why modern AI scaled when…