How Ask Equibrah Works

A large language model (LLM) is a type of AI that has been trained on massive amounts of text data to understand and generate human language. At its core, an LLM works by predicting the most likely next token in a sequence — given everything it's seen so far, what should come next?

That sounds simple, but doing it well requires the model to develop a deep understanding of context, reasoning, and domain knowledge. The model isn't looking up answers in a database — it's drawing on patterns learned during training to generate a response that fits the situation. The quality of those responses depends heavily on what data the model was trained on and how it was fine-tuned for specific tasks.

A general-purpose LLM knows a little about a lot of things. But when you take that same architecture and train it with large volumes of domain-specific data, it becomes significantly better at tasks within that domain. This is how models are specialized — a medical AI is trained on clinical literature, a legal AI on case law, and a poker AI on hand histories, strategy, and game theory.

The more high-quality, relevant data a model trains on, the better it gets at recognizing patterns, understanding edge cases, and producing outputs that an expert in that field would consider reasonable. It's not memorizing — it's learning the structure and reasoning behind the domain.

Equibrah doesn't use a single one-size-fits-all model. We use variant-specific models — one specialized for NLHE and another for PLO. The strategy, hand evaluation, and decision-making in these two formats are fundamentally different, and a model trained to excel at hold'em advice shouldn't be the same one giving you PLO guidance.

When you click "Ask Equibrah," the system automatically routes your question to the model that matches the game you're currently playing. You don't have to think about it — it just works.

Just like our equity calculations, the AI models are made aware of the HUD stats of your opponents. When there's a current aggressor — someone who bet or raised — the model receives their stats as well, giving it direct insight into whether you're facing a tight player who rarely bluffs or a loose cannon who could have anything.

This means the advice isn't generic. A recommendation to fold against a nit's check-raise means something very different than folding against a whale's check-raise — and the model knows the difference because it has the data to distinguish between them. The more hands your HUD has tracked on an opponent, the more informed the AI's guidance becomes.