I never thought I'd be writing about the challenges of being recognized in public. A year ago, I was just another data scientist trying to build connections and establish myself in the field. Now, I'm grappling with the unintended consequences of my growing online presence. Here's how it all unfolded, and what I've learned about the price of distribution in the digital age.
In the past year, I've done a lot of consulting on helping companies improve their RAG applications. One of the biggest things I want to call out is the idea of topics and capabilities.
I use this distinction to train teams to identify and look at the data we have to figure out what we need to build next.
In his essay "What's Going On Here, With This Human?", Graham Duncan discusses the importance of seeing people clearly, both in the context of hiring and in understanding oneself. He suggests asking the question "what's going on here with this person in front of me?" and provides a framework for expanding one's ability to see others more clearly. Inspired by this essay, I asked myself some probing questions to better understand my own strengths, weaknesses, and motivations. Here are my reflections:
In the next 6 to 8 months, RAG will be used primarily for report generation. We'll see a shift from using RAG agents as question-answering systems to using them more as report-generation systems. This is because the value you can get from a report is much greater than the current RAG systems in use. I'll explain this by discussing what I've learned as a consultant about understanding value and then how I think companies should describe the value they deliver through RAG.
Rag is the feature, not the benefit.
Data literacy is an essential skill in today's data-driven world. As AI engineers, understanding how to properly handle, analyze, and interpret data can make the difference between success and failure in our projects. In this post, we will explore ten common pitfalls that lead to data illiteracy and provide actionable strategies to avoid them. By becoming aware of these mistakes and learning how to address them, you can enhance your data literacy and ensure your work is both accurate and impactful. Let's dive in and discover how to navigate the complexities of data with confidence and competence.
I'm really not qualified to give advice.
But enough people DM'd me on Twitter, so here it is. I don't have to answer the same question over and over again. After some more editing I realised that I am actually writing this for my younger sister Katherine.
Don't read this if you're seeking a nuanced perspective
These are simply the lies I tell myself to keep on living my life in [good faith](https://en.wikipedia.org/wiki/Bad_faith_(existentialism)). I'm not saying this is the right way to do things. I'm just saying this is how I did things. I will do my best to color my advice with my own experiences, but I'm not going to pretend that the suffering and the privilege I've experienced is universal.
I've enjoyed writing about my indie consulting process. Today, I want to share some insights on marketing, positioning, and scaling my consulting business. This post builds on my previous discussions about referrals and the tools I use, focusing on how I've grown my practice through strategic marketing efforts.
As a former MLE, I had always relied on my hands-on coding skills to excel in my career. However, a hand injury forced me to adapt and pivot towards consulting. In this essay, I share my journey of becoming a successful AI consultant and the lessons I've learned about marketing, positioning, and scaling my business.
This is part of a series about my experience with a repetitive strain injury (RSI) during the COVID-19 pandemic. It talks about how this injury changed my life and what I think about now. If you want to know more about what happened, you can read part one here. I'm not going to give a lot of medical details. Instead, I want to look at how this injury changed what's important to me and how I live my life.
A comment on Twitter made me want to write this part of the series. I'm not trying to tell anyone how to live. I just want to think about the values that shape me and other men like me. This connects to some ideas I wrote about in my article on data literacy, where I talk about how we understand and use information in our lives.
This is the final entry (for now) of a series I wrote about my experience with tendonitis in both my wrists and thumbs during COVID-19. The injury occurred in 2020 due to overworking at my previous job, where I would put in excessive hours for weeks on end with minimal breaks. The debilitating pain prevented me from eating, dressing myself properly, and programming, leading to a significant disruption in my work and personal life. Ultimately, this experience led me to make three major changes:
Consulting full-time: Sometimes, you need to take a leap of faith; other times, you need a swift, firm shove in the back to overcome your limitations. The RSI was the latter that lead me down the path of higher leverage.
Revaluating my values: This doesn't necessarily mean discarding the traits that led to me getting an RSI, but it certainly made me rethink what is actually valuable and what I am trying to do with my life. Sorry to disappoint, but there has been no 'epiphany'. I am not discarding worldly possessions and pursuing a spirit quest to find 'my true self'. My hands are now a limited commodity, and I fear reinjuring myself. This means if I'm going to use my hands, it must be purposeful and deliberate.
Writing: Thankfully, we live in an era where you no longer need hands to express yourself through text. Writing helped me realize that I have spent my first three decades learning and absorbing knowledge and information. Now, in my thirties, my goal will be to share the thoughts and stories I've collected.
You can read the previous two entries here: Losing My Hands, Losing My Hands Pt. 2: The Two Jasons.
Prompt optimization is the process of improving the quality of prompts used to generate content. Often by using few shots of context to generate a few examples of the desired output, then refining the prompt to generate more examples of the desired output.