What if scaling context windows isn’t the answer to higher accuracy?

We’ve seen LLMs push context window limits to 1 million tokens. Impressive? Sure. But let’s get real: enterprise-scale AI systems demand more than brute force. Feeding terabytes of data into a massive context window isn’t just inefficient—it’s unsustainable.

Here’s the reality: large context windows face diminishing returns. Models struggle with the "lost in the middle" problem, where accuracy drops as critical details in mid-sections of long inputs are overlooked. Add latency, computational costs, and memory overhead to the mix, and you’re left with a bottleneck—not a breakthrough.

So, what’s the alternative? We say GraphRAG.

Unlike traditional RAG systems that rely on flat text retrieval, GraphRAG integrates structured knowledge graphs, enabling LLMs to navigate relationships between entities and concepts.

This approach addresses three core issues:

• Efficiency: By retrieving only relevant subgraphs, GraphRAG reduces token usage and slashing latency and costs.
• Explainability: Knowledge graphs provide traceable reasoning paths—critical for debugging and compliance.
• Complex Reasoning: GraphRAG enables multihop reasoning across interconnected data, outperforming vector-based systems in nuanced queries.

The takeaway? Scaling context isn’t about size.

How are you tackling these challenges in your systems?