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.
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.
When I talk to engineering leaders struggling with their AI teams, I often hear the same frustration: "Why is everything taking so long? Why can't we just ship features like our other teams?"
This frustration stems from a fundamental misunderstanding: AI development isn't just engineering - it's applied research. And this changes everything about how we need to think about progress, goals, and team management. In a previous article I wrote about communication for AI teams. Today I want to talk about standups specifically.
The ticket is not the feature, the ticket is the experiment, the outcome is learning.
Helping software engineers enhance their AI engineering processes through rigorous and insightful updates.
In the dynamic realm of AI engineering, effective communication is crucial for project success. Consider two scenarios:
Scenario A: "We made some improvements to the model. It seems better now."
Scenario B: "Our hypothesis was that fine-tuning on domain-specific data would improve accuracy. We implemented this change and observed a 15% increase in F1 score, from 0.72 to 0.83, on our test set. However, inference time increased by 20ms on average."
Scenario B clearly provides more value and allows for informed decision-making. After collaborating with numerous startups on their AI initiatives, I've witnessed the transformative power of precise, data-driven communication. It's not just about relaying information; it's about enabling action, fostering alignment, and driving progress.
As I've shared insights on indie consulting, marketing strategies, and referral techniques, a recurring question from my newsletter subscribers is about pricing. Specifically, many ask if they should lower their rates or make them public.
In this article, we'll delve into the counterintuitive reasons why listing your consulting prices might not be the best strategy, regardless of whether you're aiming to appear affordable or exclusive. We'll explore the potential drawbacks of transparent pricing, introduce more effective alternatives like minimum level of engagement pricing, and provide actionable strategies to help you maximize your value and earnings as a consultant.
Building on the foundation laid in my previous posts about building a consulting practice and using the right tools, this piece will add another crucial element to your consulting toolkit: strategic pricing.
Ever think, "This AI companion sounds odd"? You're onto something. Let's explore naturalistic dialogue and how it could change our digital interactions.
I've been focused on dialogue lately. Not the formal kind, but the type you'd hear between friends at a coffee shop. Conversations that flow, full of inside jokes and half-finished sentences that still make sense. Imagine if your AI companion could chat like that.
This post will define naturalistic dialogue, characterized by:
We'll then examine AI-generated dialogue challenges and propose a solution using chain-of-thought reasoning and planning to craft more natural responses.
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.
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.
You need to be taking advantage of your users wherever possible. It’s become a bit of a cliche that customers are your most important stakeholders. In the past, this meant that customers bought the product that the company sold and thus kept it solvent. However, as AI seemingly conquers everything, businesses must find replicable processes to create products that meet their users’ needs and are flexible enough to be continually improved and updated over time. This means your users are your most important asset in improving your product. Take advantage of that and use your users to build a better product!
Technological advancements have always been met with a mix of skepticism and fear. From the telephone disrupting face-to-face communication to calculators diminishing mental arithmetic skills, each new technology has faced resistance. Even the written word was once believed to weaken human memory.
| Technology | Perceived Threat |
|---|---|
| Telephone | Disrupting face-to-face communication |
| Calculators | Diminishing mental arithmetic skills |
| Typewriter | Degrading writing quality |
| Printing Press | Threatening manual script work |
| Written Word | Weakening human memory |