If you’ve struggled to take your LLM project from prototype to production, and even tried RAG but still didn’t achieve the accuracy you needed, it might be time to consider GraphRAG. GraphRAG combines the power of retrieval-augmented generation with the structure of knowledge graphs, delivering more reliable and accurate results.

Concentrate on the fundamentals first. Don’t get distracted by side projects. AI needs data! You can now buy general intelligence, but only you can provide your private data. However, every organisation's data is currently disconnected and poorly organised. If you want to use all this intelligence in a way that is meaningful to your organisation, then you must first get your data into a shape that is ready for use with AI.

As we move into the next phase of AI adoption, where organisations must feed their own data into large language models to realise their benefits, it's crucial to model the relationships explicitly. Large language models thrive on these relationships; that's where the power lies.

While LLMs are capable of performing inductive reasoning, they will likely struggle with true deductive reasoning. There is, however, a caveat: LLMs may learn to mimic deductive reasoning so convincingly that it becomes difficult to tell whether they are truly reasoning or merely simulating it. Currently, many AI labs are likely training the next generation of models on large reasoning datasets, hoping that, with sufficiently vast datasets, deep networks, and human reviewers, these models will approximate reasoning to a degree that is functionally indistinguishable from true reasoning. Huge amounts of money and resources are being spent on the bet that simply scaling up will work