Our AI tools? They're amazing, truly powerful. But the way we have to actually use them today can be a total pain in the ass.
Think about it: every careful prompt you craft, every bit of background you have to spell out, every time you're stuck trying to fix a conversation that completely derailed—that's all just clear proof the design is failing us.
I've been calling this the "translation tax"—the mental energy we spend translating our human intentions into machine-readable instructions.
The elimination of this tax is the key to making AI truly accessible and useful for everyone.
Why This Happens
While the push towards artificial general intelligence (AGI) aims to create AI that can understand, learn, and apply knowledge across a wide range of tasks, today's LLMs are fundamentally constrained by their architecture.
This occurs because LLMs operate within a "context window" straitjacket—a finite memory limit. This window defines the maximum text the AI processes at once. When a conversation exceeds this limit, older parts fall out of memory.
Most AI calls are also stateless. Each request is a fresh start.
The Core Problem
The application must manually stitch together conversation history and re-feed it into every prompt. This process is inefficient and fragile. If the application misses history, or if history gets too long for the context window, the AI loses its thread.
The burden of maintaining coherence falls on the developer and, ultimately, on you, the user.
This isn't solely a technology problem—it's a design problem. For users, it's cognitive drain, frustration, and wasted time. For platforms, it means higher computational costs from repeatedly sending large context windows, increased latency, and a fragile system prone to errors when context breaks.
We need to create interfaces where technology adapts to human cognition, not the reverse.
Context Engineering
Context Engineering helps bridge this translation tax by leveraging a systematic approach to providing AI systems with the behavioral guides, professional knowledge, and context that can enable it to collaborate across facets of a project.
“The aim is to achieve maximum context transfer with minimum cognitive overhead. I call this lowering the translation tax.
Let me be precise about what I mean by "translation tax." It's the cognitive effort required to translate human intent into system-compatible instructions. This tax shows up everywhere in computing:
- Spreadsheets: You pay it by learning formulas and functions
- Programming: You pay it by learning syntax and APIs
- AI systems: You pay it by becoming a prompt engineer
But AI has made this tax particularly visible and expensive. To get useful output from ChatGPT or Claude, you must learn to think like the machine thinks, structure your requests in ways that align with how the AI processes information, and maintain context across conversations.
A New Way Forward: Lowering the Threshold
That's why I started building Contextify: A tool designed to make AI interaction intuitive by automating the behind-the-scenes context and guardrails needed for effective conversations.
Automated Context Assignment
Contextify's engine automatically recognizes your intent and leverages its built-in knowledge to dynamically assign and manage the appropriate Personas, Modes, and Toolboxes (which are just pre-packaged sets of dynamic internal instructions).
This handles the "aversion to too much control" side. For most interactions, the user simply expresses their desire (e.g., "Help me plan my workout"), and Contextify's intelligence silently applies the necessary layers to guide the AI's response, eliminating the translation tax.
How It Works
The AI will ask you 5 of the most relevant questions to support your task. It'll generate a context toolbox that you can interface with through the platform or tweak to be more relevant to you.
Visible and Tunable Layers
Unlike black-box AI systems, Contextify makes these "rails"—the underlying Your Instructions, Context Modes, Modes, and Personas—explicitly visible and adjustable.
This handles the "desire for control" side without the burden. Users can:
- See what instructions the AI is operating under at any given moment
- Override a default Persona or Mode with a single click if the AI isn't quite hitting the mark
- Fine-tune specific parameters within a Mode or Toolbox
- Add new information to their Persona or current session instructions seamlessly
By exposing these layers, Contextify transforms the hidden complexities of AI context management into an intuitive, tunable interface.
“Think of it like a professional camera. Most people just want to point and shoot, and Contextify excels at its "automatic mode." But a professional photographer wants to precisely adjust ISO, aperture, and shutter speed. Contextify provides those granular controls, transparently, but doesn't force the user to touch them for every shot.
The Goal: Effortless Intelligence
The challenge is to create powerful AI that doesn't feel like a chore to use.
Good design "disappears"—you're focused on your work, not the tool itself. That's the idea behind automatic context assignment.
The pursuit of AGI often focuses on breakthroughs in model architecture or scale. However, Contextify highlights that part of the AGI challenge is also a design problem. True general intelligence won't feel truly "intelligent" if interacting with it is a constant chore.
The Real Goal
The goal is for users to develop a relationship with their work, not with the AI system. The AI should simply become a transparent medium for your expertise.
This points to a future where interfaces fade away, and the effort required to interact with computers approaches zero.
Ultimately, the drive towards general intelligence isn't just about building smarter machines; it's about building machines that are effortlessly intelligent in interaction with humans.
Real-World Impact
These ideas are already being put into practice in industries like finance and manufacturing, where AI needs to understand complex requirements. By eliminating the translation tax, we empower people to focus on strategic thinking, creativity, and innovation.
