Rotary position embedding

What it means

It improves long-context generalization without fixed positional tables. In models workflows, rotary position embedding often shapes model capability and fit.

How it works

Model architecture and scale determine capability. Context length, parameter count, and modality support vary across models.

Why it matters

Model architecture affects capability, context length, and speed.

Common use cases

• Choosing a model that fits latency and cost constraints.
• Selecting longer context for document-heavy workflows.
• Using specialized models for code, vision, or speech.

Example

Used in many long-context LLMs.

Pitfalls and tips

Bigger is not always better. Match the model to the task and evaluate in production.

In BoltAI

In BoltAI, this shows up in model selection and configuration.