Context Engineering
The craft of deciding exactly what an AI assistant sees before it answers, which shapes whether your brand gets named and booked
Context engineering is the practice of deciding exactly what information an AI assistant gets to see before it writes a reply.
Models don't "know" things on demand.
They answer based on what's loaded into the context window at that moment: the system prompt, the chat history, and whatever data the app pulled in.
Engineers tune that mix so the assistant gives the right answer instead of a vague or wrong one.
Why it matters for the ChatGPT funnel
This is the under-the-hood lever behind whether you get picked.
When a buyer asks ChatGPT for a recommendation, the assistant picks from what's in its context.
If your brand, your offer, and a way to act are sitting right there, you're a live option.
If they're not, you don't exist in that answer, no matter how good your product is.
Good context engineering is the difference between being a footnote and being the booked meeting.
How drio fits
You don't have to build any of this.
When the chat client calls your drio app, drio hands the assistant clean, well-shaped context: who you are, what you offer, and a button to book.
That's the work that turns a passing mention into a captured lead, handled for you.
Win the answer, not just the ranking
drio turns the ChatGPT and Claude conversations your buyers are already having into booked calls. Build the app that gets you picked.
Sell inside ChatGPT