The Era of Context Engineering

We went from ‘every generative AI startup is a ChatGPT wrapper’ back in 2023, to ‘every agentic AI startup is a Claude wrapper.’

And yet, this marked a key shift, something much deeper than the architectural change from prompt engineering to context engineering.

A couple of days ago, in a post on X, Andrej Karpathy highlighted something quite important.

This observation captures more than just a shift in criticism—it reveals a fundamental transformation in how AI applications are built.

The journey from simple prompt optimization to complex context engineering represents one of the most significant architectural evolutions in modern software.

The dismissive “wrapper” label persists, but the reality has evolved far beyond what critics understand.

We’ve moved from applications that simply format and relay prompts to sophisticated systems that orchestrate entire information architectures.

This isn’t about writing better questions anymore—it’s about building the cognitive infrastructure that enables AI to operate autonomously and effectively.

This analysis synthesizes Andrej Karpathy’s mental model of context engineering with Gennaro Cuofano’s observations on the evolution of AI applications, incorporating the architectural impact of the Model Context Protocol (MCP). The shift from prompt to context engineering, now accelerated by standardized protocols, represents not just a technical evolution but a fundamental transformation in how we architect intelligent systems.

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The Architectural Revolution: From Prompts to Protocol-Driven Context

People associate prompts with short task descriptions you’d give an LLM in your day-to-day use.

This mental model, which involves typing a clever question into ChatGPT and receiving a response, defined the early era of generative AI applications.

Prompt engineering meant crafting better questions, adding “think step-by-step” instructions, or including a few examples. It was simple, accessible, and fundamentally limited.

In every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step. This isn’t wordsmithing—it’s information architecture at scale.

The shift from prompt to context engineering transforms AI applications from simple query-response systems into sophisticated autonomous agents capable of complex, multi-step reasoning and execution.

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