“A black box.” That was my candid assessment of Large Language Models (LLMs) for a long time. Despite my background in building neural networks, I viewed Generative AI primarily as a productivity hack—sophisticated autocomplete that was impressive, but too prone to hallucination for mission-critical enterprise applications.
I went into Google’s deep learning boot camp to challenge that skepticism. I wanted to know if we were building on a foundation of hype or a new substrate of computing.
I left with a completely different mental model. The breakthrough wasn’t seeing the models get bigger; it was seeing how we can control them.
The Strategic Pivot: From Generation to Orchestration
The boot camp dismantled the idea that we are at the mercy of the model’s training data. We explored techniques that turn these “black boxes” into transparent, architectural components.
For product leaders, two specific architectures fundamentally change the unit economics of software:
1. RAG: Solving the Customization Dilemma
We explored Retrieval Augmented Generation (RAG), which grounds the model in external, verifiable data.
Implication: RAG solves the “Enterprise Customization” problem at scale. Traditionally, building a workflow tool that adheres to the unique compliance policies of a Fortune 500 client required massive custom development. With RAG, we don’t rewrite code; we simply connect the model to the client’s own policy documents. The model generates compliant workflows dynamically. This shifts the value proposition from “software that works” to “software that adapts.”
2. Reasoning Chains: The Audit Trail for AI
We also harnessed Chain-of-Thought and ReAct prompting techniques. Chain-of-Thought instructs the model to to output intermediate reasoning steps which reduces hallucination. Where as ReAct (Reason and Act) allows the model to decide when to perform actions (like searching or running code) before answering [https://github.com/ysymyth/ReAct].
Implication: This moves AI from a “trust me” system to an auditable partner. In high-stakes decision-making, we cannot accept black-box answers. By forcing the model to “show its work” and execute deliberate actions, we start to create the reliability and interpretability required for the boardroom.
The New Literacy for Product Leadership
This experience crystallized a shift in the product management capability stack.
Ten years ago, the most effective product managers were those who learned SQL. They didn’t wait for data science teams; they mined their own insights to drive decision velocity.
LLM orchestration is the new SQL.
Tomorrow’s product leaders won’t just write specs; they will architect intelligence. Imagine firing up a vector database, embedding thousands of unstructured customer support tickets, and querying: “What are the hidden friction points for our APAC enterprise users?” This capability allows us to move from analyzing metrics (what happened) to analyzing meaning (why it happened) at a scale that was previously impossible.
What does this mean for the exec team?
The question is no longer “Can a machine understand meaning?” The question is “Can your organization harness this meaning to see around corners?”
We are moving from an era of Predictive AI (what is the next word?) to Agentic AI (what is the next move?). Leaders who understand the architecture of these systems—who understand the difference between a raw model and a RAG pipeline based workflow —will have a distinct advantage in risk assessment, scenario planning, and product innovation.
I walked into the boot camp a skeptic. I walked out a builder. The tools to transform our products are here; it is now a matter of intentionally embracing them.
How is your organization moving beyond the “hype” phase of AI adoption?