The news
A technical breakdown from the WhoIsJsonAPI blog draws a sharp line between prompt engineering and context engineering — arguing that reliable AI systems require systematic management of what information the model receives, how memory is structured, and how context is maintained across multiple inference calls. The piece digs into token selection, memory hierarchies, and architectural patterns for keeping LLM outputs stable in production.
Our take
Most GTM teams building AI workflows are still thinking at the prompt level. They write a good instruction, the output looks great in a demo, and they ship it. Then three weeks later, the automation starts producing garbage — wrong tone, missing fields, hallucinated company names — and nobody knows why.
That's a context problem, not a prompt problem.
Here's the consistent pattern with marketing and RevOps teams: the automation works fine when the input is clean and the session is fresh. It breaks when the model is carrying stale context, when it's being asked to hold too much state across too many steps, or when upstream data changes and nobody updated what the model actually knows about the account, the persona, or the campaign.
Context engineering is the discipline of fixing that before it happens. It's the difference between a workflow that works in a demo and one that's still working six months later. That means thinking deliberately about what information you're feeding the model at each step, what it needs to "remember" across a sequence, and what you're pruning so you don't blow the context window on tokens that don't matter.
Frameworks like LangChain exist to help manage this. But the architectural thinking has to come first — and that requires GTM teams to treat their AI workflows as stateful systems, not one-shot prompts with a Zapier trigger attached.
The teams who treat context engineering as an advanced topic for later are the same teams who will be debugging flaky automations in six months wondering why their AI "stopped working."
So now what?
- Audit your existing automations for context drift: If a workflow runs on enriched CRM data, ask when that data was last validated. Stale input is the most common source of unreliable output.
- Map what each step in your AI sequence actually needs to know: Not everything needs to be in the prompt. Tighter context windows mean more stable, cheaper, faster outputs.
- Treat memory as a design decision, not a default: If your tool lets you configure what the model "remembers" between steps, that's a context engineering lever — use it deliberately.
The teams getting reliable results from AI aren't writing better prompts. They're building better pipelines.