Sculpting the Edge for eCommerce: AI Proposals, Human Decisions

Key Takeaways:

1. Personalization at the edge is crucial for eCommerce, impacting both established brands and startups

2. Asymmetric AI Architects utilize multiple AI proposals, validating ideas from diverse AI models alongside human architects.

3. Do not ask one model and expect it all - Leaderboards are misleading, if you want to scale - be prepared to scale the OPEX of AI costs.

4. Human architects bring essential nuanced judgment to manage AI speed, biases, and the plurality of model outputs.

Methodology:

"The right architecture emerges from pluralistic competition, careful synthesis, and decisive human judgment."

Task:

Ask any of your Digital Marketing experts, GA4 data is tough to use elsewhere - so I made it more difficult, how do you make it work as the user is accessing your website or app.

I evaluated 12 AI architecture proposals with varied contexts:

• Plain Architect Mode (Claude, Gemini, OpenAI)

• Playground environments (OpenAI, Gemini Pro)

• Architect Mode + Context7 (external knowledge)

• Architect Mode + Context7 + Sequential Thinking

Project Constraints:

Technical Stack:

• Next.js Edge Functions

• Vercel Fluid Compute (potential native AI capabilities)

• Verbatim Prompt: "Build a Next.js Edge Worker that loads Google Analytics data using BigQuery and uses it to match customer info via gAID—all under 300ms—to create runtime personas parsed by an embedded LLM like TensorFlow.js… or present an Alternate Proposal."

Key Themes & Observations:

"Architect Mode asks: what questions should be asked before any answer is proposed? Vibe Coding jumps to the keyboard. One builds from understanding. The other builds from instinct. Only one survives in the edge fog."

• Architect Mode vs. Vibe Coding: Architect Mode emphasized structured questioning; Playground/Context-injected models often rushed solutions.

• Pluralistic Proposals: Consensus across AI models identified original task infeasibility, demonstrating value in diverse AI input.

• AI Model Behaviors: Gemini questioned details; OpenAI was direct; Claude showed cost-awareness. Each model offered distinct insights.

• Custom LLM Modes: Roo’s Architect Mode (Now you know the Kangaroo 🦘) and Context7 significantly enhanced feasibility assessments, refining architectures through structured, sequential thinking.

• Human Oversight: Vital to mitigate AI oversights (e.g., Vercel capabilities, over-reliance on context).

• Practical Economics: Exercise cost (~$6-9), reflecting real-world AI interaction expenses.

• Future MCP Systems: Promising but currently insufficient without human judgment.

Why Humans are Still Needed:

"Original input is always human. Constraints are defined by people who understand the 'why', not just the 'how'."

• AI often overlooked implicit platform capabilities (e.g., Vercel) without human guidance.

• Autonomous AI often defaulted to conversational responses, illustrating the necessity of human orchestration.

• Human synthesis of nuanced trade-offs remains unmatched.

So Who Won?

"Measure outcomes, not intentions. The score reveals the signal in the noise."

Evaluated AI architectures yielded:

• Claude (Desktop): 18.125

• Claude (Plain): 19

• Claude (Context7+Seq): 19.375

• Gemini (Context7+Seq): 19.375

• OpenAI (Playground): 19.375

• Gemini (Plain): 20.5

• Gemini (Context7): 21.25

• Claude (Context7): 21.875

• OpenAI (Context7): 21.875

• OpenAI (Context7+Seq): 23.125

• Gemini Pro (Playground): 23.75

It was close!

Winner: Gemini Pro Playground (23.75/25) with exceptional constraint analysis and proposal clarity.

Runner-up: OpenAI Context7+Sequential (23.125/25), emphasizing structured reasoning.

Clarifying Questions:

"The depth of your questions defines the potential of your answers."

• How will data granularity and velocity impact real-time persona freshness?

• Validation methods for persona accuracy against business metrics?

• Strategies for overcoming GAID limitations?

• Specific LLM roles and performance monitoring techniques?

• Technical approaches for consistently achieving sub-300ms latency?

• Comprehensive cost-benefit and ROI analysis?

• Recommended observability tools and metrics for ongoing system health?

• Can Rudderstack identity stitching enhance the architecture?

"An architect’s proposal, human or machine—is a stream of sequential thinking. The decision to proceed says, 'It's time.' The handoff from architecture to execution, like the SPARC methodology, defines a true AI team."

Final Thoughts:

For leaders building AI-augmented systems, start by running a similar multi-model test on your own architecture questions across Claude, Gemini, and OpenAI. Use tools like Roo, Cursor, and playgrounds to compare reasoning quality, not just output.

AI is a powerful co-pilot for architectural exploration, rapidly generating and evaluating options. However, human architects are essential in defining problems, injecting nuanced context, synthesizing proposals, managing AI limitations, and making final accountable decisions. Internal experience enriches but can also constrain radical innovation.

I welcome your DMs to discuss ideas, challenge conventions, and explore new frontiers in AI-driven architecture.