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2024

Content Creation Mastery: 9 Strategies to 10x Your Impact

Look, creating content that actually matters is hard. Here's how to do it without the bullshit. These strategies apply whether you're writing tweets, creating consulting content, or building your personal brand:

  1. Titles That Demand Attention: Your title is the gatekeeper. Make it count or no one will read your shit.

  2. Hook with a Powerful Intro: You've got 15 seconds. Don't waste them.

  3. Use Evidence, Not Adjectives: "Our platform is blazing fast" means nothing. "3ms average response time" does.

  4. Foreshadow Value: Tell them exactly what they'll get. No vague promises.

  5. Structure for Scanners: People skim. Deal with it. Use headers, bullet points, and short paragraphs.

  6. Make It About Them, Not You: No one cares about your journey. They care about their problems.

  7. Be an Oracle: Predict future challenges. Be right more often than not.

  8. One Clear Call-to-Action: What do you want them to do? Ask for it. Once.

  9. Iterate Based on Data: If it's not working, change it. Ego has no place here.

Implementing Naturalistic Dialogue in AI Companions

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:

  1. Contextual efficiency: saying more with less
  2. Implicit references: alluding rather than stating
  3. Fragmentation: incomplete thoughts and imperfections
  4. Organic flow: spontaneity

We'll then examine AI-generated dialogue challenges and propose a solution using chain-of-thought reasoning and planning to craft more natural responses.

Everything I Learned from AI Consulting

As I've shared insights on building a consulting practice, marketing strategies, and referral techniques, I've realized there's so much more to discuss about the world of AI consulting. This post aims to consolidate everything I've learned, building upon the foundation laid in my previous articles. Whether you're just starting out or looking to refine your approach, this comprehensive guide will provide valuable insights into the consulting process, from positioning yourself as an expert to closing high-value deals. For those interested in the tools that support my consulting work, don't forget to check out my article on the consulting stack I use.

These insights are drawn from my extensive experience, including a livestream I conducted on the topic. If you're interested in the original discussion, you can find the YouTube live stream here.

Best Tools for Indie Consultants

As an indie consultant, having the right tools can make or break your business. Over the years, I've refined my "consulting stack" - a collection of software and services that keep my operations smooth and professional. This post is an extension of my thoughts on AI consulting and freelancing in the AI gold rush.

In this guide, I'll share the key components of my stack and why they matter. Whether you're just starting out or looking to optimize your existing practice, these tools can help streamline your operations and enhance your professional image. I'll cover everything from setting up your business foundation to essential software for day-to-day operations.

By the end, you'll have a comprehensive todo list for setting up your business foundation and implementing your own consulting stack. This advice stems from my personal experience and lessons learned from AI consulting, aimed at helping you avoid common pitfalls and accelerate your success in the consulting world.

  • in RAG
  • 4 min read

Optimizing Tool Retrieval in RAG Systems: A Balanced Approach

RAG Course

This is based on a conversation that came up during office hours from my RAG course for engineering leaders. There's another cohort that's coming up soon, so if you're interested in that, you can sign up here.

When it comes to Retrieval-Augmented Generation (RAG) systems, one of the key challenges is deciding how to select and use tools effectively. As someone who's spent countless hours optimizing these systems, many people ask me whether or not they should think about using retrieval to choose which tools to put into the prompt. What this actually means is that we're interested in making precision and recall trade-offs. I've found that the key lies in balancing recall and precision. Let me break down my approach and share some insights that could help you improve your own RAG implementations.

In this article, we'll cover:

  1. The challenge of tool selection in RAG systems
  2. Understanding the recall vs. precision tradeoff
  3. The "Evergreen Tools" strategy for optimizing tool selection
  • in RAG
  • 4 min read

The RAG Playbook

When it comes to building and improving Retrieval-Augmented Generation (RAG) systems, too many teams focus on the wrong things. They obsess over generation before nailing search, implement RAG without understanding user needs, or get lost in complex improvements without clear metrics. I've seen this pattern repeat across startups of all sizes and industries.

But it doesn't have to be this way. After years of building recommendation systems, instrumenting them, and more recently consulting on RAG applications, I've developed a systematic approach that works. It's not just about what to do, but understanding why each step matters in the broader context of your business.

Here's the flywheel I use to continually infer and improve RAG systems:

  1. Initial Implementation
  2. Synthetic Data Generation
  3. Fast Evaluations
  4. Real-World Data Collection
  5. Classification and Analysis
  6. System Improvements
  7. Production Monitoring
  8. User Feedback Integration
  9. Iteration

Let's break this down step-by-step:

How I want you to write

I'm gonna write something technical.

It's often less about the nitty-gritty details of the tech stuff and more about learning something new or getting a solution handed to me on a silver platter.

Look, when I read, I want something out of it. So when I write, I gotta remember that my readers want something too. This whole piece? It's about cluing in anyone who writes for me, or wants me to write for them, on how I see this whole writing product thing.

I'm gonna lay out a checklist of stuff I'd like to have. It'll make the whole writing gig a bit smoother, you know?

Living My Best Life: A $20 Million Daydream

I\'ve been playing a little thought experiment lately: If I had $20 million in the bank, how would I want to live my life? It\'s not about the money per se, but about imagining a life where financial constraints aren\'t the primary driver of my decisions. Here\'s what I\'ve come up with.

On Getting Recognized: The Unexpected Price of Online Success

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.

Art of Looking at RAG Data

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.