Beyond Prompts: The Hidden Threads Guiding AI Decisions
Why the information you don’t see matters more than the prompt you write
The Judge’s Dilemma
Picture two judges reviewing identical evidence in the same case. One sits in a progressive urban district with strong rehabilitation programs. The other presides in a county where “tough on crime” wins elections. Same law, same facts, very different sentences.
The difference? Context—the invisible framework that shapes every decision.
The law doesn’t change between courtrooms, but the entire ecosystem around that decision does. Lawyers understand this. They spend weeks not just arguing law, but carefully constructing the lens through which that law gets interpreted.
Your prompt is just the question being asked. Context is the entire courtroom.
This is exactly what’s happening with AI. While we’ve been obsessing over perfect prompts, the real power has shifted to context engineering—and most people haven’t caught on yet.
Context: The Decision Architecture
Every AI interaction starts not with your prompt but with an invisible information environment assembled for your query. Your question gets processed through layers of data about who you are, what you’ve done, what you prefer, and how you typically think.
Shopify’s CEO captured this shift well: “Replace prompt engineering with context engineering—the art of providing all the context needed to make any task solvable.”
Here’s a perfect example: Gemini is currently winning the coding assistant battle, even though Claude is technically more sophisticated. Why? Gemini’s massive context window lets it see your entire codebase at once, while Claude works with smaller fragments. The technically superior model loses to the one with better context.
This pattern repeats everywhere: mediocre models with excellent context regularly outperform brilliant models with limited context.
The Invisible Layer
Modern AI systems don’t just respond to your prompt—they respond to your prompt plus everything else they can access about you and the world.
When you ask ChatGPT for restaurant recommendations, it might search Yelp, check your location, remember you mentioned being vegetarian three conversations ago, and filter for your typical price range. Perplexity constructs a temporary knowledge environment from 5-10 sources, weighted by recency and reliability.
Think of RAG (Retrieval-Augmented Generation) like a lawyer preparing for trial. Instead of relying purely on memory, they pull relevant case files, precedents, and evidence for each specific argument. Your AI does the same—assembling custom information portfolios for every query.
The result? AI that operates in carefully curated information environments that shape every response.
The Control Problem
Let’s be clear: this isn’t a conspiracy. These context layers are invisible because showing users every database query and relevance score would be overwhelming and unhelpful. But it does create a genuine issue: we’re losing visibility into how context shapes AI decisions.
Your morning AI news summary feels neutral, but the context engine pulls from sources that align with your past interests, filtered through engagement patterns. Your AI operates in a curated reality you never explicitly designed.
The risk is subtle but real: AI systems that reinforce existing biases and patterns without us realizing it.
As AI handles more decisions, the entities controlling context gain significant influence over our choices—often invisibly.
Two Kingdoms of Context
Context engineering plays out across two main domains:
Enterprise Context: Your company’s processes, culture, knowledge, and workflows. When AI understands your org chart, speaks your industry language, and accesses your internal systems, it becomes genuinely valuable. This explains Microsoft’s aggressive AI strategy—if companies start feeding their organizational context to competing AI systems, Microsoft risks losing its enterprise software dominance.
Personal Context: Your habits, relationships, preferences, and behavioral patterns. The more comprehensive this becomes, the better AI gets at anticipating what you need and how you prefer it delivered. It’s not just about having your data—it’s about understanding your decision-making patterns.
Both domains use similar technical approaches but serve very different purposes.
The Consumer Battleground
The consumer AI race matters because winning means more than just acquiring users—it means becoming the lens through which people view their options.
Google knows your searches. Meta knows your relationships. Apple knows your daily patterns. Amazon knows your purchasing behavior. OpenAI is pushing hard with ChatGPT because they recognize the opportunity: become the primary context layer for everyday decisions.
We’ve moved from “content is king” to something more subtle: context shapes not just what you choose, but what choices you even see.
When AI suggests career moves, investment strategies, or even weekend plans, the company controlling that context influences your perception of what’s possible.
Shaping AI Reality
Context engineering represents a fundamental shift from simply instructing AI to creating the information environments where AI operates. The prompt becomes just the final trigger in a much more complex system.
Forward-thinking companies are already adapting. Instead of perfecting prompt templates, they’re investing in richer context through curated knowledge bases, documented processes, and integrated information systems.
Most users think they’re having straightforward conversations with AI. In reality, they’re communicating through AI within contexts they may not fully understand.
Here’s how to take more control:
Ask your AI to explain what sources it’s using
Test the same prompts with different context setups to understand how responses change
Choose AI tools that offer transparency about their context sources
For businesses: develop a clear context strategy before committing to AI vendors
So stop asking “how should I phrase this?” but ask instead “what information environment is shaping this response?”
And perhaps more importantly: “Who owns my context?”