When most teams build RAG systems for financial research, they throw documents at a vector database and hope for the best. The project behind 10-K-able took a different path—one that any serious AI engineer should study closely.

Why Structured Data Grounding Changes Everything

The key insight from this project is elegantly simple: grounding your agent with structured data beats naive RAG by seven times. That's not a marginal improvement—that's an order of magnitude difference in quality. The approach leverages SEC 10-K filings and other financial documents, but crucially, it doesn't just dump them into a vector store. Instead, the system extracts and structures the data first, creating a foundation that the agent can actually reason over.

Andrew Ng's Skills Map Meets Real Code

This project takes Andrew Ng's AI Engineering Skills Map—the six core competencies for building production AI applications—and turns theory into measurable code. The author didn't just read about eval-driven development; they built it from scratch with real benchmarks. That means actual test cases, actual metrics, and actual performance data that proves whether the approach works or doesn't.

The Eval-Driven Development Philosophy

What makes 10-K-able worth studying is its commitment to evaluation as a first-class concern. Too many AI projects skip this step entirely or treat evals as an afterthought. But if you want verifiable, production-ready agents, you need rigorous testing frameworks that measure quality before deployment—not after your users start complaining on Twitter.

Key Takeaways

  • Structured data grounding outperforms naive RAG by 7× in financial research tasks
  • Eval-driven development turns abstract AI principles into measurable outcomes
  • Andrew Ng's six skills for AI engineering provide a practical roadmap, not just theory
  • Building verifiable agents requires upfront investment in evaluation infrastructure

The Bottom Line

The hype cycle around AI agents has created plenty of vaporware. Projects like 10-K-able cut through the noise by focusing on what actually works—grounded reasoning over structured data and rigorous measurement over hand-waving. If you're building financial AI systems, study this approach before you ship another vector database experiment.