Context, not compute, will define the next generation of intelligence
For years, progress in AI has been measured in scale: larger models, larger datasets, longer pop-ups. Each new advance promises that if we simply feed more data to the systems, we will get more accurate information.
However, at least outside of training, this hypothesis faces difficulties. As models absorb longer prompts, they often become less reliable. The model has more choices, making it more likely to focus on the wrong thing.
Researchers call this context rot: as an AI system processes more information, irrelevant details clutter its working memory. This can result in less accurate responses, higher costs and a gradual erosion of trust.
A recent Microsoft experiment to create an AI-led “magnetic marketplace” demonstrated how AI can fail here. The laboratory’s general director, Ece Kamar, explained: “We find that the current models are really overwhelmed by too many options. »
How context rot sets in
Most business data resides in documents: PDFs, reports and internal files that are broken into pieces for retrieval augmented generation (RAG). When a user asks a question, the system retrieves passages that seem semantically similar and sends them to the Large Language Model (LLM) as context.
The catch is that similarity is not the same thing as relevance. A fragment may look like a match but lack key definitions or exceptions. Without additional context, a fragment may be just noise.
The AI ends up juggling too much information, without understanding which parts are really important and which just create more noise in the system.
The solution is not to insert more text, but to find text that is more relevant to the business question at hand. This means equipping AI with a layer of knowledge that reflects how the world actually works, as a network of entities and relationships, not disconnected data points.
Think in connections, not documents
Humans do not reason in documents, but in relationships. A knowledge graph explicitly captures these connections: people, places, products, and the links between them.
When data is stored and searched as a graph, the retrieval changes from “closest approximate match” to “best supported answer”. A legal assistant, for example, might ask questions about a contract clause.
A keyword or vector search might return a clause that seems relevant, while a graph-based system understands that the clause belongs to a broader definition and retrieves all related sections. The response is more complete and contextualized, which avoids the problem of trying to connect information between different blocks.
The end result is that the model needs far fewer tokens to generate a relevant response.
Why Charts Build Confidence
Transparency is another major advantage of graphics. Vector embeddings, the mathematical process that AI uses to connect similar words, are powerful for machines but completely unreadable for humans.
On the other hand, a graph is easy to see and understand. It records the exact chain of facts used by the system to reach a conclusion, as well as the sources and authorizations involved. This can be visualized in a way that makes sense to humans.
This traceability is invaluable in regulated environments. It’s much easier to justify a decision when you can show the path taken through the data and why a decision was made, rather than just pointing to a group of opaque numbers. Built-in governance and explainability make graph-based AI business-ready and trustworthy.
Don’t wait for GPT-6
Some executives wonder why they should worry about context when future models will be smarter. It is true that large language models improve rapidly. But no matter how skilled they are, they will never be trained on your private company’s data.
A foundation model also functions a bit like a search engine with extraordinary reasoning capabilities but without an index of your business information. It can generate answers, but without being fed the right context, it can’t know which parts of your knowledge are authoritative, up-to-date, or most relevant to the question.
Even when LLMs reach double-digit levels, they will still need a structured and secure way to access what is unique about a company.
This is why the bottleneck for AI adoption is shifting from computing power to data organization. The key question is no longer “Which model should I use?” » It’s “How well organized is my knowledge?”
Make charts easier to use
Graph databases once had a reputation for being difficult to learn. This was true ten years ago, when teams had to invent their own schemes from scratch. Two changes have made them much more accessible.
First, Graphics Query Language (GQL) is now an international ISO standard. It is the first new data language to be standardized since SQL several decades ago. GQL gives engineers a shared declarative language for working with graphical data, one that complements SQL rather than competing with it.
Standardization leads to improved interoperability, clearer documentation, and a well-defined skill set for hiring purposes.
Second, thanks to AI, modern graphics platforms now automate jobs that previously required specialized expertise. Assisted modeling, domain models, and hybrid search, which seamlessly blend vector and graph queries, are now powered by AI and accelerated with agents.
This is a step change aimed at making technology easier to use and deploy. Teams spend less time manually creating data structures and more time asking real business questions.
The Knowledge Layer Advantage
Smart organizations are realizing that the best results from AI come from combining powerful models with well-organized, connected, and contextualized knowledge. The model is the engine of reasoning; the chart is the scaffolding that holds the right facts in place.
When retrieval is driven by connections, it produces higher quality context and better outcomes. LLMs can devote less effort to filling in gaps and more to providing precise, explainable reasoning. Responses improve, latency decreases, and costs decrease. More importantly, users start to trust the answers.
We are moving from an era defined by raw calculation to an era defined by organized context. Longer prompts and larger templates will continue to be important, but structure, clarity, and connectivity will matter more.
If you want AI that is consistent, fast, and trustworthy, the path forward is not “bigger.” It’s better organized.
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