Context is the vessel of the AI Native era
In the AI Native era, deep mining of user context is the key to success.
Core content:
1. The core difference of AI Native products: mining of user context
2. The multi-dimensional value of context: from project architecture to real-time traffic conditions
3. The methodology and challenges of building context: from information collection to dynamic reflux
1. The sea level is rising, don't rush to build a "high tower"
The capabilities of the underlying large model are like sea water, getting higher day by day. Today you use prompt-hack (or complex workflow) to build a "tower", and tomorrow the native functions of the model will raise the water level above the top of the tower. Search is the most typical example - when Deep (Re)Search becomes the default skill of the model, your little idea of feeding keywords into the API will have no barriers.
If you want to survive the rising tide, you need a " ship ". What is a ship? It is a product capability that can rise with the sea water - that is, the mining and construction of Context.
2. Context is not just a "chat context"
Many people think of Context too narrowly and only focus on a conversation History. But the Context that can really determine the outcome is often hidden in deeper dimensions:
project architecture, code dependency tree, and
real-time traffic conditions when the user is driving that day.
AI does not know the mood or related content of potential consumers when browsing products.
The hidden knowledge scattered in Excel and emails within the enterprise
is unknown to AI, but users know it. This information gap is what we want to grab.
3. Orthogonal information gap: The new division of labor between users and AI
pulls "users know/AI knows" into four quadrants. The most valuable is the one that users know but AI does not know. Willingness, background, off-site conditions... If they cannot be captured efficiently and sent to the model, no matter how large the parameters are, it will be useless.
So the roles began to reverse: in the past, AI assisted people, and in the future it will be more like people assisting AI. The user's task is to provide as orthogonal and scarce background as possible, and the model is responsible for decision-making and execution.
4. Constructing Context: Methodology and Pitfalls
Information collection: sensors, buried points, natural language forms, let users "painlessly" contribute to the background.
Screening and integration: vector database + knowledge graph, filter out noise, and star high-value fragments.
Dynamic reflux: use the results to feed back the collection logic to form a feedback loop.
The difficulty is also obvious:
privacy and trust: the more private the context is, the more sensitive the user is.
Motivation design: No one wants to fill out forms without immediate benefits.
Time management: Once the background information expires, it is more terrible than lack of information.
5. The future winner
When the parameter scale approaches the cost ceiling, the next round of competition will see who can achieve the ultimate in context management:
Collection/update efficiency
Interaction threshold (less typing, zero configuration)
Domain depth (vertical industry know-how)
Ecological integration (using plug-ins to turn external tools into context sources)
In the final analysis, context is the means of production. Only by abstracting the information gap into ship planks can we still float steadily on the crest of the wave when the sea rises next time. The group of people who worked hard to build the tower in vain can only watch the base being submerged by the tide, and then sigh that "the parameters have become stronger again."
Less structure? More structure? This is the design knowledge of the "captain", and the executor only needs to focus on one thing: let the ship dance with the sea level.
