I had a conversation with my friend today that shook something loose in my head: no one has potential. Like most of the lies I tell myself, this is obviously false - and yet, sometimes we need these extreme statements to see a deeper truth.
We often combat excess pessimism with excess optimism. We see potential in others and believe they can change. But this is just a projection of our own potential and values and beliefs.
Let me explain.
I want to invite my lawyer, Luke, to talk a little bit about the legal side of consulting. If you're new you should also checkout our consulting stack post.
In August, Luke officially launched Virgil. Their goal at Virgil is to be a one-stop shop for a startup’s back office, combining legal with related services that founders often prefer to outsource, such as bookkeeping, compliance, tax, and people operations. We primarily operate on flat monthly subscriptions, allowing startups to focus on what truly moves the needle.
He launched Virgil with Eric Ries, author of The Lean Startup, and Jeremy Howard, CEO of Answer AI. He's able to rely on the Answer AI team to build tools and help him stay informed about AI. He's licensed to practice in Illinois, and they have a national presence. That's his background and the essence of what we're building at Virgil.
There's a reason Google has separate interfaces for Maps, Images, News, and Shopping. The same reason explains why many RAG systems today are hitting a performance ceiling. After working with dozens of companies implementing RAG, I've discovered that most teams focus on optimizing embeddings while missing two fundamental dimensions that matter far more: Topics and Capabilities.
"Those who can't do, teach" is wrong. Here's proof: I taught at the Data Science Club while learning myself. If I help bring a room of 60 people even 1 week ahead, in an hour, that's 60 weeks of learning value creation. That's more than a year of value from one hour. Teaching isn't what you do when you can't perform. It's how you multiply your impact.
Its a duty.
Retrieval augmented generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by integrating them with external knowledge sources. In essence, RAG combines the generative power of LLMs with the vast information stored in databases, documents, and other repositories. This approach enables LLMs to generate more accurate, relevant, and contextually grounded responses.
This article explains six proven strategies to improve Retrieval-Augmented Generation (RAG) systems. It builds on my previous articles and consulting experience helping companies enhance their RAG applications.
By the end of this post, you'll understand six key strategies I've found effective when improving RAG applications:
Picture this: You're sitting at your desk, contemplating the leap into AI consulting. Maybe you're a seasoned ML engineer looking to transition from contractor to consultant, or perhaps you've been building AI products and want to branch out independently. Whatever brought you here, you're wondering how to transform your technical expertise into a thriving consulting practice.
I want to share something that completely changed my consulting business: writing consistently.
Last month, a founder reached out saying, "I don't know who you are, but your blog posts keep showing up in our team's Slack. Are you available to help us?"
Two days later, we closed a $140,000 deal (for a 3-month project). Only 3 sales calls were needed.
This wasn't luck – it was the compound effect of putting words on the page every single day.
In the next year, this blog will be painted with a mix of technical machine learning content and personal notes. I've spent more of my 20s thinking about my life than machine learning. I'm not good at either, but I enjoy both.
I was born in a village in China. My parents were the children of rural farmers who grew up during the Cultural Revolution. They were the first generation of their family to read and write, and also the first generation to leave the village.
Have you ever wondered why some teams seem to effortlessly deliver value while others stay busy but make no real progress?
I recently had a conversation that completely changed how I think about leading teams. While discussing team performance with a VP of Engineering who was frustrated with their team's slow progress, I suggested focusing on better standups and more experiments.
That's when Skylar Payne dropped a truth bomb that made me completely rethink everything:
"Leaders are living and breathing the business strategy through their meetings and context, but the people on the ground don't have any fucking clue what that is. They're kind of trying to read the tea leaves to understand what it is."
That moment was a wake-up call.
I had been so focused on the mechanics of execution that I'd missed something fundamental: The best processes in the world won't help if your team doesn't understand how their work drives real value.
In less than an hour, I learned more about effective leadership than I had in the past year. Let me share what I discovered.