Why Graph RAG is the Future
Standard RAG is like reading a book one sentence at a time, out of order. We need something new.
When you read a book, you do not jump randomly between paragraphs, hoping to piece together the story. Yet that is exactly what traditional Retrieval-Augmented Generation (RAG) systems do with your data. This approach is fundamentally broken if you care about real understanding.
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What is Actually Happening in Standard RAG?
Most RAG systems take your documents and chop them into tiny, isolated chunks. Each chunk lives in its own bubble. When you ask a question, the system retrieves a handful of these fragments and expects the AI to make sense of them. The result is a disconnected, context-poor answer that often misses the bigger picture.
This is like trying to understand a novel by reading a few random sentences from different chapters. You might get a sense of the topic, but you will never grasp the full story or the relationships between ideas.
Real understanding requires more than just finding relevant information. It demands context and the ability to see how pieces of knowledge relate to each other. This is where standard RAG falls short. It treats knowledge as a stack of random pages, not as a coherent whole.
Enter Graph RAG
Graph RAG changes the game. Instead of isolated chunks, it builds a rich network of interconnected knowledge. Think of it as a well-organised library, complete with cross-references and relationships. When you ask a question, Graph RAG does not just find relevant information. It understands how everything connects, delivering answers that are both accurate and deeply contextual.
This is not just theory. Research shows that Graph RAG systems can reduce token usage by 26% to 97% while delivering more accurate, contextual responses. The difference is not subtle. By understanding relationships, Graph RAG provides answers that make sense, not just answers that match keywords.1
The future of AI is not about having more data. It is about understanding the relationships within your data. If you want your AI to deliver real value, you need to move beyond standard RAG and embrace approaches that prioritise context and connection.
For more on how this fits into the broader evolution of AI agents and knowledge management, see Building the Future.
How are you managing data retrieval within your AI system?
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For a deeper dive into the research, see these papers: Comparative Analysis of Retrieval Systems in the Real World and GraphRAG: Graph-based Retrieval-Augmented Generation ↩
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AI: The New Dawn of Software Craft
AI is not the death knell for the software crafting movement. With the right architectural constraints, it might just be the catalyst for its rebirth.
The idea that AI could enable a new era of software quality and pride in craft is not as far-fetched as it sounds. I have seen the debate shift from fear of replacement to excitement about new possibilities. The industry is at a crossroads, and the choices we make now will define the next generation of software.
This is the moment to ask whether AI will force us to rediscover what software crafting1 truly means in the AI age.
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I use the term “software craft” to refer to the software craftsmanship movement that emerged from the Agile Manifesto and was formalised in the Software Craftsmanship Manifesto of 2009. The movement emphasises well-crafted software, steady value delivery, professional community, and productive partnerships. I prefer the terms “crafting” and “craft” to avoid gender assumptions. ↩
Introducing Kaijo: AI functions that just work
For months, I have wrestled with a problem that has consumed my thoughts and challenged everything I know about software development.
This week I wrote about building the future with AI agents. One of the key areas for me is moving beyond prompt engineering to something more reliable.
I have spent decades learning how to craft reliable software. Now I want to bring that reliability to AI development.
Today I am ready to share what I have been building in the background.
It started with a game. It ended with something that could change how we build AI applications forever.
Read moreBuilding the Future
Something has been on my mind for months. The rapid evolution of AI agents has opened up possibilities I cannot ignore.
We are witnessing the emergence of semi autonomous agents that will fundamentally reshape how we work and communicate. The opportunities in this space are extraordinary. I am diving deeper into this world of AI agent development and product creation.
My newsletter is evolving. Instead of dispensing tips from a position of authority, I invite you on a journey of discovery. I will document my experiences building with AI, how to apply my tech experience in a new world, and navigating the inevitable struggles and setbacks.
Read on for several key areas I am exploring.
Read moreThe Reality of AI Power Usage
AI power usage generates significant controversy. Headlines paint it as an environmental catastrophe waiting to happen. The reality proves more nuanced and potentially more optimistic than these dire warnings suggest.
A ChatGPT query uses 10 times more energy than a Google search. This sounds alarming until one realises it equates to running your hairdryer for six seconds. The entire data centre industry, including all AI operations, accounts for just 1.5% of global electricity consumption.
Here is a rundown of the more pressing issues with AI power usage.
Read moreAI Therapists: Self Reflection With AI
“I am not ready for this conversation.” We all know that feeling. The endless mental rehearsal. The anxiety building with each imagined scenario.
But what if I told you that AI could be your practice partner for both difficult conversations and deeper self-reflection?
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I've dedicated myself to learning the very latest AI tools and techniques and have used my experience as a founder and CTO of multiple businesses to separate out hype from what works now.
I've applied what I've learned so far to my own businesses, and it's transforming everything. It can for you too.
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