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Genie Community

Reflections on Our First Genie Community Office Hours 🌟

Summary: Samantha Menot shared a recap of the first Genie Community Office Hours, highlighting the participation of approximately 81 attendees. The session discussed tools and features such as Ontology, Genie One, and Zero Ops, focusing on product highlights and common questions about metadata, governance, and performance. Future training sessions and community events were announced, with a survey linked for attendee feedback. Abdelhalim Dadouche expressed enthusiasm for future sessions, to which Samantha Menot responded with a promise of upcoming details.
AI Summary

🎉 Recap: Our First Genie Community Office Hours (8 July 2026) - If you attended please complete our survey now: https://surveys.training.databricks.com/jfe/form/SV_3ekm8gNElFRUf30

Thank you to the ~81 of you who joined our very first Genie Community Office Hours! This community exists to connect practitioners and customers of every skill level - to learn together, share best practices, and build champions with Genie. Amazing energy in the chat - so much so that we couldn't get to every question live, so we've captured a summary below.

A huge thanks to the all-star Databricks team for hosting and supporting with all of the Q&A: Christophe Chieu, Elizabeth Abraham, Kevin Risch, Lingeshwaran Kanniappan, Sivakumar Ganesan, and Youssef Mrini.

📚 Training & enablement — AI/BI & Genie Summer Camp - coming week of July 20 - more details to come

- AI/BI for Data Analysts — 4-hour virtual ILT

- Genie Producer — 4-hour session on building conversational agents (SQL proficiency recommended)

- Genie Consumer — 3-hour workshop for non-technical business users

💡 Organizations with 100+ interested participants can request a free, dedicated training session — reach out if that's you. Registration links and the full July schedule will be posted here on the forum.

🧠 Product highlights

- Ontology — a live, self-improving context layer that closes the "business context gap." Using a PageRank-inspired algorithm to rank business rules and definitions, it reportedly lifted AI accuracy from ~50% to 84.5% while reducing token costs. Connects to sources like email, Google, and SharePoint.

- Genie One — a unified environment to ask natural-language questions, schedule tasks, and work with data on mobile. Conversations can be turned into agents shared company-wide via Slack or Teams.

- Gen Code & Gen Builder — natural-language code/pipeline generation and app-building (even from screenshots), with native Unity Catalog integration for table discovery and security.

- Zero Ops — automated debugging: clones pipelines to find/fix errors (pushable via CI/CD) and scans ML models on metrics like precision/recall to flag issues such as overfitting.

- Terminology update: "Genie Spaces" are now "Genie Agents," reflecting new capabilities like task scheduling and automation.

❓ Your questions from the chat & survey

On metadata & accuracy

- Can Genie scan the underlying data to auto-generate accurate metadata instead of relying on existing metadata? (Mani) — Genie uses schema info, sample values, relationships, and Genie Code suggestions to generate context automatically, but it still depends on curated metadata and human review. Better auto-generated metadata improves results, but it doesn't fully replace a well-curated semantic layer.

- Isn't the point of Ontology to remove these "manual" steps? (Gary) — Ontology automates building the knowledge graph and context mapping, but you still input the domain-specific business rules that are unique to your organization.

On managing instructions & business rules

- We're hitting a "Long Text" warning putting non-SQL business rules into the instruction set — does this hurt token costs, and are there alternatives? (Gary) — Keep instructions concise and keyword-driven; split Genies if exceptions get too broad. Use compaction to summarize tokens, and expose complex logic as Python functions via Unity Catalog rather than long free-text.

- Our own benchmark query keeps getting evaluated as "bad," which lowers accuracy — how do we deal with that? — Great one we want to dig into further; we'll follow up on the forum with guidance (see open items below).

On governance, permissions & scaling

- Consumer-permission vs. creator-permission modes can mean two users see different data — can we pin a Genie to a fixed set of assets so everyone consumes from the same source? — Genie One may route queries to different Agents based on a user's permissions. Best practice: one Genie per subject area for a consistent, trusted source across all business users.

- How do we layer an agentic/Genie experience over an existing Databricks App running predefined SQL, and get governed answers without hand-coding every query? (Akash) — Use Unity Catalog Metrics for authoritative, governed responses, then run the loop: deploy to business users → monitor responses → refine instructions/metrics.

- As we scale to hundreds of Spaces and thousands of users, which DE/governance decisions most affect accuracy, performance, and adoption? — Limit tables per Genie (aim for ~5–6), use serverless SQL warehouses, and distribute workloads across workspaces.

On performance

- For very large Delta tables, does Genie automatically use Photon, Liquid Clustering, materialized views, or caching? — Performance is handled by the selected SQL warehouse (Photon, liquid clustering, caching). Adjust warehouse size / increase compute for high concurrency or slow responses.

On usage tracking & budgets

- Genie usage isn't tagged in system.billing.usage — how do I build a monitoring dashboard? (David) — You can track non-billing usage via the Audit access tables (these include Genie One chats; Genie Code has a separate table). Isolate Genie Spaces onto separate compute to loosely link access usage to billing. For spend, the built-in Budget UI tracks usage by user/group and lets you set thresholds — no custom dashboard needed. (Thanks to the community member who shared the Audit-tables tip!)

- We set a budget threshold and see values appearing — does the dashboard show only pay-as-you-go, and does data appearing mean the free monthly allowance is used up? (Alciona) — Correct: the Budget dashboard only shows consumption above your free monthly allowance, so records appearing means you've passed the free tier.

On pricing & free usage

- Is Genie pay-per-DBU / can we use it without paying? Effective 6 July 2026, each user gets 150 DBUs of free LLM usage per month — is there a cap on how many users can use the free tier? (Indra) — There's a free monthly allowance per user; the Budget UI shows consumption above it. We'll confirm the specifics on per-user limits and post the details on the forum.

On migration

- Can we migrate Tableau dashboards (with actions and parameters) to AI/BI? (Sugandha / Indra) — Yes — Genie skills can facilitate the migration, and AI/BI dashboards support parameters. See the import guide in Resources below.

On automation & DABs deployment

- Can Genie detect data accuracy in a table, and is automation possible in a Genie Space? (Artem) — Genie Agents now support task scheduling and automation.

- When deploying Genie Spaces via DABs: JSON config doesn't support variable interpolation, and Spaces aren't bind resources (each deploy replaces the Space and loses history) — any roadmap for these? — This is a great product/roadmap question we're taking back to the team; we'll follow up on the forum.

📎 Resources shared in the session

- 📝 Introducing Genie One, Genie Agents, and Genie Ontology (blog) (https://www.databricks.com/blog/introducing-genie-one-genie-ontology-and-genie-agents)

- 📝 Introducing Genie ZeroOps (blog) (https://www.databricks.com/blog/introducing-genie-zeroops)

- 📘 Genie best practices (https://docs.databricks.com/aws/en/genie/best-practices)

- 📘 Genie Code skills (https://docs.databricks.com/aws/en/genie-code/skills)

- 📘 Audit access tables (usage tracking) (https://docs.databricks.com/aws/en/ai-bi/admin/audit)

- 📘 Unity Catalog metric views (https://docs.databricks.com/aws/en/business-semantics/metric-views/)

- 📘 Migrate Tableau reports to AI/BI dashboards (https://learn.microsoft.com/en-gb/azure/databricks/dashboards/manage/import-bi)

- 🎓 Training: Building Reliable Conversational Agents with Genie (https://www.databricks.com/training/catalog/building-reliable-conversational-agents-with-genie-5222)

🔎 Open questions we're following up on

- The "benchmark query evaluated as bad" accuracy issue

- Per-user limits on the free LLM/DBU allowance

- DABs roadmap: JSON variable interpolation & bind-resource support for Genie Spaces (to preserve history across deployments)

We'll post answers to these here as we get them.

📌 What's next

- 📅 Next Office Hours in a couple of weeks (scheduled to be West Coast-friendly) — we're exploring a weekly cadence based on your demand.

- 🎤 Our first Genie Community event is being planned in partnership with Advancing Analytics, featuring speakers from customer organizations.

- 📝 Please complete the post-session survey (https://surveys.training.databricks.com/jfe/form/SV_3ekm8gNElFRUf30) - it directly shapes what we build next.

The forum is home base - please keep the questions and conversations going here so the whole community benefits. Thanks again, and see you at the next one! 🚀

4 comments

Looking forward attending the next one!

We will share more details soon!

Thank you.

And thanks for sharing.