Chat Formatting handles the technical layer between human-readable conversation and model-optimized input. Different models require different formatting — role markers, system prompts, conversation boundaries, and special tokens. This layer ensures that regardless of which foundation model is handling a request, the conversation is formatted optimally for that model's architecture and training.
Each foundation model has optimal input formats. Chat formatting automatically adapts conversation structure to match the active model's requirements.
Manage what information is included in each model call. Prioritize recent, relevant context within token limits while preserving essential background information.
Construct system prompts dynamically from role definitions, brand guidelines, task instructions, and contextual information — creating rich, relevant initial context.
Properly delineate conversation turns, system messages, and tool results to prevent context confusion and maintain clear interaction flow.
Chat Formatting works in concert with other layers in the intelligence stack — each connection amplifying the capability of both components.
Ensure optimal AI performance regardless of which model handles the request. Chat formatting provides the translation layer that lets your intelligence stack work seamlessly across multiple foundation models.
Discover how Chat Formatting fits into your enterprise intelligence strategy.
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