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  • 6 min read

Context Engineering Series for Agentic RAG Systems?

I've been helping companies build agentic RAG systems and studying coding agents from Cognition, Claude Code, Cursor, and others. These coding agents are likely creating a trillion-dollar industry—making them the most economically viable agents to date.

This series shares what I've learned from these teams and conversations with professional developers using these systems daily, exploring what we can apply to other industries.

If you want hands-on help, I recommend reaching out to my friend Nila: nila.is. Please mention you came from me.

Related Series

Coding Agents Speaker Series: Deep insights from the teams behind leading coding agents including Cognition (Devin), Sourcegraph (Amp), Cline, and Augment. While this Context Engineering series focuses on technical implementation patterns, the Speaker Series reveals strategic insights and architectural decisions.

RAG Master Series: Comprehensive guide to building and scaling retrieval-augmented generation systems. Context Engineering principles directly enhance RAG implementations—structured tool responses and faceted search are foundational RAG optimization techniques.

  • 18 min read

Why Is Context Engineering the Future of RAG?

The core insight: In agentic systems, how we structure tool responses is as important as the information they contain.

This is the first post in a series on context engineering. I'm starting here because it's the lowest hanging fruit—something every company can audit and experiment with immediately.

  • 195 min read

Frequently Asked Questions

This comprehensive FAQ is compiled from all office hours sessions across multiple cohorts.

Quick Navigation

Use your browser's search (Ctrl+F) to find specific terms or questions, or browse through the questions below.

  • 5 min read

RAG for Coding Agents Lightning Series

I find this to be a pretty interesting topic because I personally believe that coding agents are probably executing at the frontier of agentic ray systems.

The world of autonomous coding agents is rapidly evolving, with fundamental disagreements emerging about the best approaches to building reliable, high-performance systems. This Lightning Series brings together the minds behind some of the most successful coding agents—from SWE-Bench champions to billion-dollar products—to debate the core architectural decisions shaping the future of AI-powered development.

If you just want to sign up, you're going to have to visit every single tab, open these links, and sign up to each one.

  • 5 min read

Lovable, Monetization, and the Vibe Coder Economy

| These are all just notes from a 30-minute conversation I had with somebody. A fun little exercise, as you will see.

When people ask me what a hot take is, here's mine: more agent tools and AI tools should be pricing on outcomes and trying hard to figure out what that means. This aligns with my broader thoughts on pricing AI tools as headcount alternatives.

The question hit me personally as a small investor in Lovable and a consultant focused on value-based pricing: Why am I not building my consulting business, my courses, my job board on Lovable instead of spreading them across Stripe, Maven, Circle, Kit, and Podia, It's because I could only possibly pay $100/month, and for that, they could not possibly offer me the features I need to.

  • 12 min read

RAG Anti-Patterns with Skylar Payne

I hosted a Lightning Lesson with Skylar Payne, an experienced AI practitioner who's worked at companies like Google and LinkedIn over the past decade. Skylar shared valuable insights on common RAG (Retrieval-Augmented Generation) anti-patterns he's observed across multiple client engagements, providing practical advice for improving AI systems through better data handling, retrieval, and evaluation practices.

  • 9 min read

How to invest in AI w/ MCPS and Data Analytics

tl;dr: You should build a system that lets you discover value before you commit resources.

!! Key Takeaways

Before asking what to build, start with a simple chatbot to discover what users are interested in. There's no need to reach for a complex agent or workflow before we see real user demand.

Leverage tools like Kura to understand broad user behavior patterns. The sooner we start collecting real user data, the better.

This week, I had conversations with several VPs of AI at large enterprises, and I kept hearing the same story: XX teams experimenting with AI, a CEO demanding results for the next board meeting, sales conference, quarterly review, and no clear path from pilot to production.

These conversations happen because I built improvingrag.com—a resource that helps teams build better RAG systems, which has lead me into many conversations from researchers, engineers, and executives. But the questions aren't about RAG techniques. They're about strategy: "How do we go from experiments to production?" "How do we know what to invest in?" "How do we show ROI?"

  • 11 min read

Systematically Improving RAG with Raindrop and Oleve

I hosted a lightning lesson featuring Ben from Raindrop and Sid from Oleve to discuss AI monitoring, production testing, and data analysis frameworks. This session explored how to effectively identify issues in AI systems, implement structured monitoring, and develop frameworks for improving AI products based on real user data.

  • 4 min read

Pricing AI Agents, Headcount, and the Economic Reality

Today I spoke to an executive about SaaS products, and they told me something that shifted my perspective entirely: AI agents need to be compared to budgets that companies draw from headcount, not tooling.

This is one of those insights that seems obvious in retrospect, but completely changes how you think about positioning AI tools in today's market—especially in this era of widespread tech layoffs and economic uncertainty.