Why LLMs Need Better Context?

In a previous post, we’ve discussed how context is king for GenAI apps and how LLMs without proper context are absolute garbage machines. Back to my parrot analogies: That’s like training a parrot to repeat flight instructions and then expecting it to land a plane.

Context is what makes a difference between an LLM that provides insightful answers and one that confidently spews nonsense. Every LLM has an increasingly bigger and bigger, but still super hard, limit on how much text it can process at once—its context window. Here’s why that’s a problem…

3 Reasons why LLMs needs better context

Context Window Limitations

If you try to cram too much information into the limited context window, older details get dropped. That means long-tail dependencies—like tracking fraud over multiple transactions—just vanish. Summarizing vast datasets becomes a game of broken telephone, where accuracy drops and errors creep in. Ever had an LLM forget what you said three messages ago? That’s the context window at work.

Fine Tuning is a Money Pit

One way to make LLMs “smarter” (again with that word -.- ) is fine-tuning, but it’s far from an ideal solution. First off, it’s expensive and slow. You need serious compute power and engineering chops, brains, time and resources to tweak a model for your specific use case. Can you imagine a non-FAANG enterprise doing that continuously? And even then, once it’s fine-tuned, your model is basically frozen. If new data comes in, you’re stuck retraining or, worse, working with an outdated model. If your business relies on real-time insights, this approach is basically useless.

Security Risks and LLMs Leaking Internal Corporate Data

LLMs predict text, not rules, which makes them risky in enterprise settings. Without proper safeguards, they can regurgitate training data, including sensitive or proprietary information in the responses they give out. If you are persistent enough, they can also be tricked into oversharing through insecure prompts. Unless you have fine-grained access control, you’re rolling the dice every time you use an LLM in production.

What can you do about this? Give LLMs a “brain” in the form of a real-time graph.

5 Reasons to Give LLMs better Context

Private: Set up LLMs to reason over your own data

Instead of dumping all your data into an LLM, why not let it reason over a structured, real-time knowledge graph? With Memgraph, you can query proprietary data dynamically without exposing it. That means no more hardcoding facts into a model—just pulling in relevant, up-to-date context when needed.

Personalized: Context-backed answers, not just text

Not everyone in your company wants the same kind of answer, obviously. An engineer debugging logs needs different insights than a CTO assessing security risks. A CFO looking at fraud trends needs yet another perspective. Memgraph helps LLMs tailor responses based on who’s asking, ensuring that every answer is relevant and to the point.

Regulated Access: No More Accidental Leaks

With fine-grained access controls, Memgraph ensures that LLMs only retrieve what they’re supposed to. No more accidental leaks of confidential reports or sensitive customer data. If security matters to you, this is non-negotiable.

Always Fresh: Real-Time Context Injection

Static models in a dynamic world? I don’t recommend this approach. This is why businesses trying to use LLMs for mission-critical decisions—fraud detection, cybersecurity, or supply chain management—often run into trouble. LLMs aren’t built for cause-and-effect reasoning. They don’t understand relationships, just patterns.

Memgraph enables LLMs to pull in real-time data, ensuring they’re always working with the latest insights. Whether you’re detecting fraud, analyzing live data, or responding to customer queries, you need AI that can adapt on the spot.

Relevant: No More Random Noise

LLMs don’t just need any data—they need the right data. Memgraph integrates multiple retrieval sources—vector, text, geospatial, graph—and intelligently filters, ranks, and connects them. Instead of generic, one-size-fits-all responses, your LLM can surface contextually relevant insights every time.

Context, context, context. Not bigger models.

The key is feeding LLMs the right context, at the right time, in a structured way. That’s what real-time knowledge graphs can provide.

With structured, dynamic context, LLMs have a better chance of:

• Delivering accurate, up-to-date responses.
• Keeping proprietary data secure and controlled.
• Providing personalized, role-aware answers.

I say, and I hope we can agree on this:

Stop fine-tuning LLMs. Start fine-tuning how they access context!