The term 'prompt engineering' just got deprecated — context engineering is the new skill stack

The term “prompt engineering” just got deprecated. Quietly. And most people missed it.

Three unrelated builders posted the same conceptual shift in a single 24-hour window. Harrison Chase (LangChain CEO). An AI educator whose context engineering course just hit 235,000 views. A product researcher tracking agent evaluation. None coordinated. All arrived at the same place.

The primitive layer of AI system design has moved.

Before: What should I say to the model? Now: What should the model know, see, and be constrained by?

Prompts are instructions. Context is architecture. These are not the same thing.

Why this matters:

A better prompt improves one session. Better context architecture improves every session, every agent, every analysis — permanently.

Harrison Chase framed it precisely in his “Agent Development Lifecycle”: Build → Test → Monitor → Deploy. The Build step no longer means “write instructions.” It means: what does this agent know? What constraints apply? What data is in scope? What feedback loops close the system?

Viv (@Vtrivedy10) put the second half: “Think of your agent as a system that can be measured and improved. Testing starts before production.” That’s not a prompting mindset. That’s a systems engineering mindset applied to intelligence.

Anthropic just published 28 minutes of their own internal org design principles on YouTube (“Running an AI-native engineering org”). Even the people who built the model are not talking about prompting. They’re talking about context layers and org structure.

What this reframes at Manthan:

Our 107,000+ entity knowledge graph isn’t “data.” It’s the context layer.

When an analytical agent processes a deal, it doesn’t query a model’s training knowledge — it queries a verified graph of funding signals, competitive dynamics, sector patterns, and founder histories built and graded continuously. The model is nearly irrelevant. The context is everything.

Our 65.5% weighted analytical accuracy (1,370+ graded scorecards) isn’t because we chose the best model. It’s because we’ve spent months engineering the context that model receives.

The 1000x knowledge worker isn’t a better prompter. They’re a context architect.

The question for every professional right now: what is your context layer?

For investment analysis: knowledge graph + sector models + pattern library. For legal work: precedent graph + contract taxonomy + regulatory context. For strategy consulting: client history + market maps + decision frameworks.

Context engineering is the new professional skill stack. And like every real skill shift, the window to build it ahead of everyone else is closing faster than it looks.

We’re building ours at getmanthan.com. And documenting what we’ve learned.

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— Mayank Mathur, Manthan Intelligence