Dave
Dave Azure MVP

Microsoft AI Tour Chicago 2025: Field Notes from the Hub

Microsoft AI Tour Chicago 2025: Field Notes from the Hub

Microsoft AI Tour Chicago 2025: Field Notes from the Hub

How influenceable conversations and technical trade-offs shape production-ready AI solutions.

On September 25, 2025, I had the privilege of serving as a Technical Expert at the Microsoft AI Tour Chicago 2025. The AI Tour is a global series of free, in-person events designed to accelerate AI adoption by connecting practitioners, developers, and decision-makers with Microsoft experts.

On Thursday, I walked into McCormick Place before sunrise with a Sharpie, a stack of sticky notes, and a promise to myself: only conversations that help people ship real AI-based solutions.

My home base for the day was the Hub — a collaborative space where attendees bring their real-world challenges, and experts like myself help translate them into governed, production-ready solutions. In this article, I’ll share field notes, technical patterns, and actionable lessons from Chicago, designed for leaders, engineers, and practitioners aiming to scale AI responsibly.

If you were there, you know the energy I’m talking about: a cross-current of builders, leaders, and developers who came to translate AI headlines into reliable, governed, production outcomes.

For context, Microsoft AI Tour: Chicago is a free, one-day, in-person event as part of a global series designed to condense months of learning into a single day, featuring experts, deep-dive sessions, and working time. Chicago’s stop ran 7:00 a.m.–5:45 p.m. CDT at McCormick Place, 2301 S. Indiana Ave, and the Hub ran like an “always-on clinic” for 1:1 and small-group problem-solving.

Event page: https://aitour.microsoft.com/flow/microsoft/chicago26/landingpage/page/cityhome

What the Hub Really Is

Think of the Hub as the “apply” zone of the AI Tour. While the main keynotes and breakout sessions deliver vision and technical depth, the Hub is where we, Microsoft FTEs and Community Experts, staff it, diagnose, debate, and design solutions in real time.

  • Framing: What are you building? Where are you stuck?
  • Sketching: Whiteboard data sources, retrieval/indexing, model endpoints, and guardrails.
  • Pick a pattern that fits: RAG vs. fine-tune; agentic tools vs. a simple service call; Copilot extension vs. a net-new app.
  • Add guardrails that move with you: Input filters, retrieval filters, managed identities, cost/time budgets, and observable tool calls.
  • Leave with next steps + docs: Two to four actions you can execute immediately, plus official references.

That cadence turned 10–15 minutes into something far more valuable than a business card exchange.

The Conversation (that made me write this)

Mid-afternoon, an AI leader I’ll call M. arrived with a familiar look: hopeful, skeptical, and very done with slideware. We were five minutes into whiteboarding when she said, half-joking:

“I wish the whole conference were like this table.”

I asked what this meant. She said:

“A place where my idea has to survive questions, and where your idea has to be influenceable.”

That word — influenceable — stayed with me. The best moments in the Hub weren’t lectures; they were two-way edits. A team would bring a mental model; I’d bring another, and we’d splice them into a third thing that could stand up to constraints (compliance, latency, budget, legacy interoperability) and still get out the door.

The Hub exists for that exact alchemy: short cycles, sharp questions, and decisions that unblock. The day convinced me (again) that the fastest path to production isn’t bigger models; it’s better conversations.

Three Patterns That Defined the Day

1. RAG First, Fine-Tune Later

A financial services team wanted accuracy and industry tone. We separated concerns:

  • Ground truth with Retrieval-Augmented Generation (RAG).
  • Tone through prompt engineering or small fine-tunes after measuring baseline quality.

Resources:

  • RAG Pattern (Azure Architecture Center)
  • “Use your data” with Azure OpenAI
  • Azure AI Search (vector search)

2. Agents Need Guardrails, Not More Powers

One team had an “all-in-one” agent that was unreliable. Instead of scaling the model, we:

  • Limited the agent to few, typed, idempotent tools.
  • Added timeouts, retries, per-run budgets, and replayable logs.
  • Shifted from “mystery stalls” to observable workflows.

Resources:

  • Azure Well-Architected for AI
  • Azure AI Content Safety

3. “We Have PII. Can We Ship?” — Yes, With Guardrails

A health-tech team was paralyzed by compliance concerns. Our playbook:

  • Map identity to data with managed identities.
  • Scope retrieval to approved indexes only.
  • Add pre/post-generation checks for PII.

My Contribution as a Technical Expert

At the Hub, my role wasn’t to pitch products — it was to pattern-match problems to proven architectures.

  • Zero fluff: If three paths existed, I explained risks, trade-offs, and the one I’d pick.
  • Governance-first: Identity, logging, and content safety weren’t “phase two” — they were “now.”
  • Actionable hand-offs: Everyone left with immediate steps and Learn docs.
  • Cross-pollination: I redirected attendees to other experts when their edge cases fell outside my lane.

This mindset ensured trust, speed, and practical value in every exchange.

Field Notes

RAG That Scales

  • Chunking is a design decision. Over-chunk and you lose meaning; under-chunk and you drown the model in noise.
  • Hybrid search (vector + keyword) boosts precision when you can’t afford hallucinated footnotes.
  • Query transformation (rewrite, expand acronyms) is often the quiet hero of relevance.
  • Groundedness + citations shift stakeholder confidence from “magic” to “auditable.”

Read more:
👉 Build Production-Ready Multi-Agent AI on Azure AI Foundry
👉 Build a Data-Driven AI Agent with Azure AI Agent Service (Python)

Agentic Workflows Without Chaos

  • Keep tool surfaces small, typed, and observable.
  • Add budget caps and max step counts; your pager will thank you.
  • Capture tool traces so you can debug reality, not vibes.

Copilot Where It Fits

  • Hunt for workflows where 70–80% of a draft follows patterns.
  • Use least-privilege connectors and rotate secrets often.
  • Instrument accept/edit/reject to measure value beyond anecdotes.

Key resources I reused constantly:

  • Azure AI Foundry
  • Model Catalog
  • Azure OpenAI overview
  • Create/manage Azure AI Foundry hub

Governance That Moves With You

  • Map identity to data; enforce with managed identities.
  • Put checks before retrieval (index selection), during prompts (input policy), and after generation (output policy).
  • Keep a change log for prompts, embeddings, and model versions. Canary everything like code.

Why This Matters Beyond Chicago

Technology doesn’t stall adoption — conversations do. The AI Tour’s Hub is proof that the fastest way to move from prototypes to production is influenceable conversations, where every idea can be challenged and improved.

For organizations navigating AI transformation, these one-day events create urgency, foster collaboration, and deliver production-ready outcomes in days, not months.

Planning Notes (for your future self)

If you’re eyeing a future stop, here’s the logistics that mattered most in Chicago:

  • Arrive early — the schedule starts at 7:00 a.m. and the first hour is gold for calm, high-signal conversations.
  • Prioritize two “musts” — lock them in, then leave oxygen for Hub time and serendipitous demos.
  • Bring artifacts — diagrams, redacted prompts, constraints. Ten minutes become sixty when we can point at the same thing.
  • Leave with a plan — if you don’t have 2–4 next steps by the end of a conversation, ask for them.

Takeaways for Practitioners

  • Conversations beat configurations. Clear thinking > clever knobs.
  • RAG first. Ground truth, then style.
  • Constrain your agents. Less is more.
  • Governance isn’t optional. Start day one.
  • Ship a slice. Prove value in thin workflows.
  • Instrument everything. Measure quality, cost, latency, and acceptance.
  • Stay influenceable. The best models are co-authored.

Final Thoughts

Chicago reminded me why I volunteer for the Hub: a ten-minute conversation can often resolve a three-month stall.

If we met there, thank you for being open and for tackling the complex problems.

Learn docs:

— Dave R.

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