Adapter Layer

Definitions

1. A small trainable module inserted into a larger pre-trained model for parameter-efficient adaptation. It influences how models are trained, evaluated, or served, and it can materially change accuracy, robustness, latency, cost, or interpretability. Practitioners usually track it alongside data quality, compute limits, and validation results when moving models into production.

   In plain English: Adapter Layer is an AI concept that affects how a model learns, predicts, or gets deployed. It matters because it changes quality, speed, or reliability.

   Example: "We revisited Adapter Layer during model evaluation because the first run looked fine offline but behaved poorly in production, and the adjustment improved quality without breaking our latency or compute budget."

Related Terms

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• system prompt
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• Retrieval-Augmented Generation
• LoRA
• Multimodal AI
• AI Agent
• GPT Architecture
• Merging Models