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A year ago, everyone was obsessing over the perfect prompt template.
You may remember using some yourself, or having to be very specific with your wording to get the answer you want. But AI has moved past that. Models today understand intent far better than they used to, running internal reasoning steps that let them interpret what you mean even when your prompt isn’t perfect.
This shift has quietly changed how teams should think about getting value from AI. The limiting factor is no longer the prompt itself. It’s the information the model has access to when it answers, the context behind the question.
And once you see that, you understand why some teams consistently get sharp, strategic insights from AI while others end up with generic summaries that don’t help them move the business forward.
Why prompt engineering isn’t the lever it used to be
Early language models behaved like advanced autocomplete. If you didn’t phrase something correctly, the model would interpret your request literally and miss the nuance. That’s why the ecosystem evolved around prompt recipes such as role instructions, step-by-step checklists, strict structures, and magic keywords for better outputs.
Today’s models don’t work that way. Reasoning models run internal thinking steps before they respond. They break down your request, infer what you’re actually trying to accomplish, and structure the logic on their own. You no longer need to script the reasoning path manually because the model constructs its own internal chain of reasoning before it speaks.
This is why people can write vague, messy prompts and still get surprisingly strong answers. The model isn’t following your exact words, it’s following the intent it infers from them. The sophistication comes from how well the model reasons, not how cleverly you prompt it.
Which means prompt engineering isn’t the bottleneck anymore. The model has taken over that part.
If the AI handles the reasoning, what does it still need?
The new key to insightful answers is context.
A model can reason brilliantly, but it cannot reason about your world unless it knows what that world is. If we take sales as an example. No LLM knows:
- your pipeline
- your accounts and stakeholders
- your team’s performance patterns
- your products and their differences
- your deal history
- your messaging strategy
Without that information, the model is guessing. It might guess well, but the insight will always be surface-level.
This explains why two people can ask the same question and get very different levels of quality. It’s not because one wrote a better prompt. It’s because one supplied richer context.
The model can supply the reasoning, but only you can supply the relevant information about your business.
This is why the most effective GTM teams today focus less on prompt templates and more on building flows that give AI access to the right context. When models reason with complete information, they elevate from being assistants to strategic partners.
Why context has become the new GTM skill
GTM teams have spent decades building systems that contain this ‘context’: CRM fields, call notes, product documentation, deal reviews, dashboards. The problem is not a lack of information, it’s the fragmentation of it. AI can only be as insightful as the data it’s allowed to see, and for most teams, that data lives in disconnected systems that were never designed to work together.
As AI becomes better at interpreting intent, the differentiator will be how much context you can give it. That’s the new operational advantage. Leaders need to master context orchestration, ensuring the model sees the same information that a great manager would bring into a strategic conversation.
How Hive Perform brings context into the conversation
This is exactly why Hive Perform is built the way it is. Hive does not just layer AI on top of sales workflows, it keeps the model connected to the real context behind every deal. Automatically. Hive pulls in your sales playbook, product information, USPs, objection handling guidance, product messaging, and team performance patterns as they emerge. The model is continually updated with the latest information, so its reasoning when it analyses deal calls and emails stays accurate and grounded in reality.
When Hive generates an insight, a recommendation, or a coaching moment, it is not relying on a static snapshot. It is operating with the full, living context of your pipeline. The reasoning comes from the model, but the precision comes from the context Hive supplies.
The takeaway for modern GTM teams
Prompt engineering mattered when AI needed hand-holding. That era is closing. The new frontier is context.
When you give AI better context, you get better strategy.
When you give it incomplete context, you get generic suggestions dressed up as intelligence.
The teams that win with AI won’t be the ones writing clever prompts. They’ll be the ones building systems that let AI see the truth of their business.
If you want to experience AI that actually knows your business, start your free trial with Hive Perform today.
