AI Models.
How models work, what they cost to run, and how to judge whether one is right for the job.
AI Models
How a Language Model Chooses the Next Word
A model does not look up answers. It scores possible next pieces of text and picks one, again and again. That one fact explains most of its behavior.
AI Models
Context Windows: What Fits, What Gets Dropped, and Why It Matters
The context window is everything a model can use for one response. Anything outside it does not exist for the model at that moment.
AI Models
Tokens, Prices and Why Long Inputs Cost More
Models are priced and timed in tokens, not words. Knowing how text becomes tokens helps you estimate cost and avoid surprises.
AI Models
Temperature and Sampling: Controlling Variation, Not Truth
Lowering temperature makes answers more repeatable. It does not make them more correct.
AI Models
Why Models Make Things Up, and What Reduces It
A model is built to produce plausible text. Without grounding, plausible text can be wrong. Several practical measures reduce that risk.
AI Models
Retrieval or Fine-Tuning: How a Model Should Learn Your Material
Many teams that think they need fine-tuning need retrieval first. The difference comes down to whether the knowledge changes and what behavior you need.
AI Models
Evaluating a Model on Your Own Task Before You Trust It
Public scores describe public tests. Your product needs a small set of examples that look like real inputs and a clear definition of a good answer.
AI Models
Open Weights or a Hosted API: What Each Choice Commits You To
Running a model yourself gives control and responsibility. Using a hosted one gives speed and dependence. Both carry costs that show up later.
AI Models
Small Models on Local Hardware: Where They Win
A smaller model that runs on your own machine can be the right tool for private, repetitive or offline work. Its limits are predictable.
AI Models
Models That Read Images, Audio and Video: Where the Errors Hide
A model that reads a chart or a recording can be confidently wrong in ways text does not reveal. Know the usual failure points.
AI Models
Embeddings Explained: Meaning as Distance
An embedding turns text into a list of numbers so that similar meanings sit close together. Search, grouping and retrieval are built on that idea.
AI Models
Reasoning Models: More Thinking Time, Different Trade-offs
Some models spend extra steps working through a problem before they answer. That helps on some tasks and costs time and money on all of them.
