What is context engineering ?

200,000 tokens for Claude, 2 million for Gemini, 128,000 for ChatGPT Pro… Do these numbers ring a bell? No? While everyone’s getting excited about context window sizes, a disturbing truth emerges from the field: the problem isn’t how much information your AI can ingest, but the quality of what you give it and your timing.

Welcome to the world of context engineering, the lesser-known discipline that separates frustrated users from true AI power users.

The Perfect Prompt Myth

“If only I had the right prompt, my AI would be perfect!”

Monumental error. After hundreds of hours observing human-AI interactions, one reality stands out: the prompt is just the tip of the iceberg. Your AI can have the most gigantic context window on the market, but if you stuff it with inadequate or outdated information, the results will disappoint.

Imagine asking a certified accountant to advise you on investments, but only giving them your 2019 bank statements and electricity bills. No matter their expertise, their advice will miss the mark.

This is exactly what happens with most AIs: they receive too much useless information and not enough relevant elements at the right time.

Moreover, “too much info kills the info”: LLMs can’t retain EVERYTHING, so you need to write appropriately, with the right words, being as economical as possible.

The 3 Pillars of Successful Context Engineering

This is where it gets fascinating.

You can dramatically improve your LLM’s responses through context engineering.

Context engineering relies on three distinct mechanisms, each having its precise role in your AI’s information ecosystem.

Intelligent Startup Loading

First pillar: give just what’s needed to start

Unlike traditional approaches that dump 3,000-word prompts, context engineering prioritizes efficiency. The goal? Enable your AI to start a relevant conversation with minimal information.

At Nova-Mind, we range between 4,000 and 10,000 tokens at startup. This measured approach, specially designed for the Sonnet model, allows Nova to understand who you are, your main objectives, and your current context without drowning in information.

Concrete example: Instead of loading your entire professional history, Nova receives:

• Your current situation
• Your 3 priority objectives
• Your preferred communication style
• Relevant details about your recent conversations

Result? She can engage in productive conversation from the first exchange, without asking you to repeat information she should already know.

Targeted Asynchronous Retrieval

Second pillar: fetch precise info when needed

Here’s the secret no one tells you: a high-performing AI doesn’t store everything in memory. It knows where to find the information it needs, when it needs it.

This approach solves a major problem: how to access specific functionalities without polluting the context with generalist information? The answer lies in three words: intelligent asynchronous loading.

This implies your model has “agentic” capabilities, and isn’t just a conversational LLM.

Practical case: You tell Nova “let’s do accounting this morning”. Instantly, she detects the intention and automatically loads her specialized accounting prompt with all methods, shortcuts, and accounting best practices. No need to explain how to make a balance sheet or calculate VAT - she activates the expert mode suited to your request.

Evolving Vectorized Memory

Third pillar: intelligence that grows with usage

The magic happens here. The more you interact with your AI, the more her understanding of your context refines. This vectorized memory stores not raw data, but contextualized insights about your preferences, behavioral patterns, and objectives.

The advantage? Your AI develops a nuanced understanding of your professional personality and needs. She knows you prefer direct answers in the morning, that you’re more receptive to creative suggestions in the afternoon, and that certain subjects require a particular approach.

Concrete illustration: After a few weeks of use, Nova understands that when you mention “former client Dupont”, she should search your vectorized DB for specific information about that client: project context, methodology used, experience feedback. This semantic search, impossible to capture with a simple prompt, radically transforms exchange quality - Nova retrieves relevant details even if you don’t remember the exact project name.

This implies personalized processing of your data, again asynchronously. And the only model ahead of others today for agentic capabilities is Claude, not ChatGPT!

The vectorized DB can enable broad searches on your projects or context, but it also allows pattern detection that Nova-Mind can analyze to refine her behavior with you.

In other words, she learns to know you and adapts to serve your interests best.

I’ll address this point more extensively in another article about coaching…

Nova-Mind: When Theory Becomes Reality

Let’s get concrete. At Nova-Mind, we haven’t just theorized context engineering - we’ve implemented it in a system that works daily.

CRON Consolidation for Ultra-Fast Access

Key innovation: automated consolidation

We developed a CRON task system that regularly consolidates each user’s critical information. The principle? The most important and recent data is either integrated into initial loading or accessible via direct database access, bypassing vectorization.

This approach ensures Nova always has the freshest information about your ongoing projects, imminent deadlines, and current priorities. No more conversations where your AI seems to have forgotten your recent context.

Todoist Reinforcement for Task Management

Starting with the “Nova Pro” plan, you get a Todoist account for managing your tasks and calendar… Or rather, for Nova to manage it all!

You receive a schedule to enter in your planner? “Nova, add all this to my calendar and Todoist please”. Boom, in 30 seconds, all tasks are added with appropriate tags, deadlines, reminders, etc.

I’ll discuss this more in another article, but you’ll see, it’s impressive.

All this to say, this is also context, and you can ask Nova at startup to do a quick check-up on your day’s tasks.

It’s like arriving at the office and having your assistant brief you on what you need to handle for the starting day…

Concrete Usage Examples

First case: You start your day asking Nova to review your priorities. In seconds, she accesses your Todoist schedule, identifies overdue tasks, crosses this information with your quarterly objectives stored in the database, and proposes a personalized action plan. No complex prompt required.

Second case: Mid-conversation about a new project, you mention wanting to apply “the same approach as for client Dupont”. Nova immediately performs a vectorized search through your history, retrieves the methodology used six months ago, and adapts it to the current context. The efficiency of an expert who never forgets anything.

She can also create a personalized prompt with custom triggers, which she’ll immediately store in your database to retrieve the work method later when you express the need.

No more repetition, you do this naturally (“Nova, do the accounting please”).

The Future Belongs to Mastered Context Engineering

Final revelation: In a world where everyone focuses on AI model power, real productivity gains come from model evolution, but also from context engineering. We’ve proven this with Nova-Mind.

An AI that understands your context, knows where to find relevant information, and learns from your interactions is no longer an assistant - it’s a true strategic partner.

Our conviction? AI’s future isn’t played in the token race, but in contextual intelligence. And on this field, Nova-Mind has several lengths ahead.

Ready to discover what an AI that truly understands you can accomplish? Test Nova-Mind and feel the difference.