The news
A recent piece from Product Leaders Day makes the case that prompt engineering is functionally obsolete — replaced by context engineering, a more architectural discipline focused on how data is structured, retrieved, and injected into an LLM before it ever generates a response. The argument: tweaking prompt phrasing is a band-aid; context engineering is the actual system.
Our take
This framing is correct, and it matters for GTM teams more than most people realize.
Here's the practical version: prompt engineering is what happens when you write a good ChatGPT message. Context engineering is what happens when you build a system that consistently gives your AI the right information at the right moment — account history, CRM data, persona context, conversation state — so the output is actually useful and not a hallucination wearing a blazer.
The article focuses on enterprise RAG pipelines and vector databases, which can sound abstract. But this pattern shows up constantly in GTM tooling. When an AI SDR sends a follow-up that ignores everything in the deal history, that's a context problem. When a content AI keeps generating messaging that doesn't match the ICP, that's a context problem. When a summarization bot confidently invents facts about a prospect, that's a context problem. None of those get fixed by rewriting the prompt. They get fixed by rethinking what information gets passed into the system before the model does anything.
The reason "context engineering" sounds like expert-only territory is that most AI tools abstract it away — and then break silently when the context is wrong. The teams actually getting reliable AI output have, intentionally or not, started solving the context problem. They've connected their CRM. They've written system prompts that inject persona and funnel stage. They've built retrieval steps that pull relevant data before generation runs.
That's context engineering. You're probably already doing pieces of it. Now you have a name for it — and a reason to do it more deliberately.
The so-what
Most AI failures in GTM aren't model failures. They're context failures — the AI didn't have what it needed to do the job. If your AI-assisted workflows are producing inconsistent or unreliable output, don't start by rewriting prompts. Start by auditing what context the system actually has access to when it runs. The teams building durable AI systems aren't better at prompting — they're better at feeding the machine.
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