The 'Trust Me Bro' Problem of AI

So, AI. Remember when everyone thought it was going to take our jobs? Turns out, it's mostly really good at writing bad code masquerading as the work of a 10x software developer and arguing with strangers on Twitter...or at least confidently spouting nonsense with the conviction of a cable news pundit. The problem? AI's fondness for hallucinations – basically, making stuff up. The other problem? We all want agents to automate fast.

Think of it like this: you ask your teenager where they were last night, and they give you a detailed story involving rescuing kittens from a burning building... except, you know, they were at Dave & Buster's. AI is often that teenager.

But here's the thing: we need AI. In your world – whether you're wrestling with the energy sector or taming the HR beast – the potential is massive. But only if we can trust the darn thing.

Enter RAG (Not the Fabric, the AI Kind)

RAG stands for Retrieval-Augmented Generation. Think of it as giving AI a super-powered research assistant with a relentless thirst for knowledge. It scours the internet, databases, and dusty corners of your company's servers to find relevant information before spitting out an answer. It's like Wikipedia on steroids.

Why is this important?

• Factual Accuracy: AI grounded in real information is less likely to make up stories about rescuing kittens... or about the future of fracking.

• Relevance: You get answers tailored to your specific context, not some generic, one-size-fits-all response.

• Transparency: You can (potentially) see where the AI got its information, so you can judge its credibility.

Imagine a chatbot for your HR department, instantly answering questions about benefits, compliance, and company policy. Or an AI helping energy analysts predict market trends by sifting through mountains of data. That's the promise of RAG.

But Here's the Catch: RAG's "Garbage In, Garbage Out" Problem

If RAG is only as good as the information it finds, you're still screwed if the information is wrong or taken out of context. It's like relying on Yelp reviews for Michelin-star dining tips. At its core, RAG is still ‘generating.’

Enter Knowledge Graphs: The Brains Behind the Brawn

This is where things get interesting. A Knowledge Graph (KG) is like a meticulously organized, interconnected map of information. It's not just about storing data; it's about understanding the relationships between things. Think of it like this:

• Entities: People, vendors, claims (think of an insurance KG)

• Attributes: Claim date, age, address

• Relationships: Person A has agent as Vendor X

Imagine a network where concepts and entities are interconnected by clearly defined relationships. This "semantic network" allows AI to not just access data but to understand the context, infer relationships, and answer complex queries with greater precision than traditional text-based models. In other words, with KGs the AI is not predicting the word but contextualizing.

Why This Matters in Your World:

• HR: Visualize the impact of salary increases on retention rates by mapping employee data to performance metrics.

• Energy: Predict equipment failures by connecting maintenance logs with sensor data from drilling rigs.

It's like having a virtual expert who understands how all the pieces fit together, not just a robot regurgitating facts or making up answers that seem right. You need an expert and not a hustler.

The Magic Happens When They Team Up: KG + RAG = The Dream Team

RAG finds the ingredients, but the Knowledge Graph tells you how to cook them.

• Logical Consistency: KGs prevent AI from making illogical leaps. No more AI telling you that increasing employee benefits will decrease morale.

• Contextual Depth: KGs provide the "why" behind the "what."

• Hallucination Reduction: Verified, structured data means less AI-generated B.S.

• Multi-Step Reasoning: KGs can follow complex chains of thought, allowing AI to solve problems that would stump even your smartest analysts.

Real-World Examples to Make Your Head Spin (in a Good Way):

• HR: Imagine an AI that can analyze employee feedback, identify hidden patterns of dissatisfaction, and suggest specific interventions to improve morale. (Based on structured data in the KG and then enriched with RAG content)

• Energy: Envision AI that optimizes energy distribution by understanding the interplay of demand, weather patterns, and infrastructure limitations. (same pattern as HR)

The Bottom Line: You Need to Think About This

Look, I get it. It is exciting to see every other person claiming on LinkedIn that they automated their workflow and yet you find yourself demoing tools after tools only to realize that nothing is perfect. There is a reason. Everyone’s fascinated by RAG. Not many have turned their attention to KGs. Reason? No one has worked in your industry and they don’t get the sensitivity and the need to get it 100% right every time.

Consider this: if you are constantly flipping through demos and trials but the benefit of automation has not come to your doorsteps yet and you cannot let an AI loose on your data, you should consider building an internal knowledge base and letting LLMs in only as the human’s interface to the graph. Start experimenting with AI with the right approach grounded on your enterprise’s tacit, collective know-how. Train the AI to learn your industry and not just guess.

Here's Your Homework:

1. Ask your IT department, "What are we doing with AI, and how are we making sure it's not making stuff up?"

2. Think about one problem in your organization that could be solved by better data and better understanding. Could a KG-RAG approach help?

3. Most importantly, be skeptical. Don't give into the hype. Move from small wins to big wins.
   

PS: I will be in SF, San Diego Houston (of, course), New Orleans, Boston, Stamford, New York, and Chicago, all through June. I am always up for a good coffee. So hit me up if you’d like to meet.