For quite some time I have been concerned about the unrealised value in unstructured data – the myriad of Word documents and PDF’s that contain everything from organisational policies to processes and reports (we aren’t talking about video, images and audio in this article). This increasing amount of unstructured data and the ability to absorb it is one of the things that increases the time that new hires take to become effective or means that a policy (if not encapsulated within a system) does not get adhered to.
This is where RAG (Retrieval Augmented Generation) helps us – it can help with things like reducing on-boarding time for new employees and ensuring policy compliance – we can have a plain language interface to our documents that isn’t just search (which returns a list of documents that you then have to read) – it helps return knowledge, something much more useful and can be acted on.
(If you want to get under the skin here is a great article by Bijit Ghosh – HybridRAG.)
What is it?
Hybrid RAG (Retrieval Augmented Generation) is an approach that blends two different types of storage for unstructured data, graph-based retrieval with vector search. It pulls relevant information such as relationships in a knowledge graph and relationships between words to generate accurate, context-rich responses.
An example of this is Neo4j – think of Neo4j as a powerful engine that allows you to store and explore these relationships in documents allowing us to extract and use useful knowledge.
What does it mean from a business perspective?
For businesses, the implications are substantial – hybrid RAG allows you to provide more accurate insights than traditional methods.
Imagine you’re managing finances or leading a customer service team. You need accurate and useful information—some of it being clear, straightforward facts (like connections between people or companies) and other parts being less obvious (like understanding feedback from customers or trends from reports). Hybrid RAG brings these together, helping you make better-informed decisions faster.
From a business perspective, this means less time wasted on data retrieval and more time acting on reliable, context-rich information.
What do I do with it?
If you’re in a data-heavy field (who isn’t when it comes to unstructured data), now is the time to explore the potential of Hybrid RAG with something like Neo4j (or at an even higher level – Microsoft Copilot connected to your various unstructured data sources). Here are three steps to get started:
- Assess Your Data Landscape: Understand the types of data your business generates and relies on—structured (e.g., databases) and unstructured (e.g., emails, reports). This will help you identify where a hybrid approach can add value.
- Explore Neo4j’s GenAI Stack: Neo4j offers tools like the GenAI Stack. Test it out, either by setting up a demo or working with a developer to pilot a small-scale project.
- Upskill Your Team: Hybrid RAG requires a blend of skills—data science, AI, and knowledge graph management. Encourage your team to explore resources on Neo4j, LangChain, or other tools that combine structured and unstructured data retrieval.
By taking these steps, you can start to really extract value, reduce the time to effectiveness for newly on-boarded employees and realise the potential of your unstructured data.
Additional Reading
Chat with finance and operations data on Microsoft 365 Copilot
Technical – Implementing Advanced Retrieval RAG Strategies with Neo4j
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