Scaling GAI with the Right Team, Stack, and Signals
Hiring, measuring, and building your way past the prototype
Over the past few months, I’ve been helping medium to larger sized businesses build their GenAI product strategies, namely identifying the set of features that provide true value to end users and how to construct roadmaps to be flexible enough to support all the expected change in the next 2 years. This is the second article in a series, the first article focused on feature selection. This second article focuses on how to execute on the strategy and build for scale.
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Team Creation
The first step in assembling the right team, having the right people in the right roles, aligned around system integrity, measurable outcomes, and clear execution paths. If you're leading or supporting GAI initiatives, this article will help you build the foundation to scale with confidence. The roles below are essential once you move past the prototype phase:
Please Don’t Say Prompt Engineering
Harnessing Generative AI isn't about crafting the perfect prompt anymore—it's about designing intelligent, outcome-oriented systems. True value comes from building agentic architectures that combine memory, state, and dynamic tool use to deliver consistent, high-impact results.
Diagram: Anatomy of an Agentic AI System
• Planner: Breaks down objectives into actionable steps
• Memory Layer: Tracks prior interactions and decisions
• Tool Integration: Executes tasks via real-world APIs and services
• Feedback Loop: Continuously improves based on results and signals
Frameworks like LangGraph, Semantic Kernel, and ReAct aren’t just technical add-ons—they’re the backbone of scalable, autonomous AI systems. What sets successful deployments apart is not just intelligence, but how that intelligence feels: tone, context-awareness, and consistency across long conversations.
As you scale, prioritize orchestration layers and treat stateful, goal-driven behavior as a product capability—not just a technical feature. Delivering differentiated user experiences starts with agents that not only think but also connect.
Training Methods That Power Modern LLMs
A common question I get from Engineering Teams is “How do you train your model?” The answer right now is always fine tuning, reinforcement learning and RAG. To build LLMs that generate prescriptive, reliable outputs, data teams use a layered training strategy:
SFT is where the model first learns the "right" answer. Human-labeled data is the foundation.
This stage shifts the model from correctness to preference alignment—critical for subjective domains.
RAG enhances model grounding by letting it reference live or domain-specific information.
Common Breakpoints (and How to Fix Them)
Once GAI is in production, things get fragile. Do yourself a favor and be prepared. Below are the most common failure points:
Metrics That Matter (and How to Watch Them)
You can’t improve what you don’t observe. Here’s a focused set of metrics that every engineering team should track post-prototype:
Dashboards to track these metrics, built with Prometheus, Grafana, and Weights & Biases should be a default—not a future aspiration. These aren’t optional. They’re lifelines. Product and Engineering should monitor and review them together on a regular basis.
TLDR
As GenAI moves from exploration to execution, success hinges on assembling the right team, architecting for scale, and continuously measuring what matters. Prompt engineering or fancy UX alone won’t carry you—agentic design, smart orchestration, and outcome-focused thinking will. If you're building beyond the prototype, this phase is where your strategy either solidifies or starts to crack. In my next article, I’ll dive deeper into how to align your GenAI roadmap with evolving user behavior and enterprise constraints—ensuring your investments remain adaptive, measurable, and valuable over time.
Attribution and Inspiration
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