Is Prompt the New Context? Evolving Approaches to Model-Aware AI Workflows

As enterprises embed LLMs into customer service, decision support, and content generation workflows, a new realization is emerging: success depends not just on the model’s intelligence, but on its ability to understand context. This shift makes model contextualization a strategic priority, defining how LLMs interpret user inputs, apply external knowledge, and generate responses that are grounded, trustworthy, and ready for action.

This Viewpoint traces the evolution of contextualization techniques – from static, training-time fine-tuning to dynamic, real-time techniques such as prompt engineering and retrieval-augmented generation. It explains the growing relevance of long-context window models, which allow richer reasoning by holding more information in memory, and highlights the rise of protocol-based contextualization, including Anthropic’s Model Contextualization Protocol (MCP), IBM’s Agent Communication Protocol (ACP), and Google’s Agent-to-Agent (A2A), which enable persistent, interaction-aware agent ecosystems.

The report breaks down contextualization strategies and their trade-offs across latency, costs, and reasoning depth, and maps each approach to its ideal use case. Enterprises can use it to design context-aware LLM workflows that reduce hallucinations, improve response quality, and adapt in real time, paving the way for more dependable and intelligent AI systems.