# Iterative AI Mode
### Summary
Iterative mode is the fast, back-and-forth workspace for exploring a question, refining it, and checking the AI’s work before you share it. It keeps short-term memory inside the thread, returns quick previews for tables/charts to keep latency and cost low, and lets you **review & approve** results before they appear in the Message window. Scalars (e.g., “Sales for 1H 2025 was **X**”) run against full data; preview tables/charts are lightweight. Once you’re happy, approve and (optionally) send the work to **Data Studio/GenViz** to build polished analytic content that runs on the full dataset.
### How to get here (UI path)
* **Workspace → Channel → Iterative AI**
1. Open your **Workspace**
2. Choose the **Channel** bound to the right Dataverse
3. Click the **Iterative AI** tab/mode
4. Ask your question to start a thread
***
### Human-in-the-loop workflow (review → approve → share)
1. **Ask** a question and iterate: follow-ups, filters, drilldowns.
2. **Review** the answer in the thread and (where available) check the **Building** panel for logic/SQL transparency.
3. **Approve** to commit the result to the **Message** window (nothing shows to others until you approve).
4. After approval you can:
* **Publish** as a Message to the channel
* **Pin** important insights
* **Share** a view-only link
* **Clone** the Message for another audience
* **Open in Data Studio / GenViz** to build cards, summary tables, and charts (full-data execution)
***
### Data scope & performance model (preview vs full)
* **Scalars = Full data.**\
Examples: totals, averages, single-value KPIs (“Sales for 1H 2025”, “Avg tariff rate last quarter”). These are computed on the full Dataverse so the number you quote is authoritative.
* **Tables & charts = Preview by default.**\
To keep **latency low** and the **cost of iteration** minimal, tabular and visual results render from a **sampled/subset preview** while you’re exploring. This is ideal for trying different filters, time ranges, or group-bys rapidly.
* **Promote to full data when ready.**\
Once the preview looks right and you **approve**, you can:
* run the query on **full data**, or
* **hand off to Data Studio/GenViz**, where the visualization/metric is materialized against the full dataset and saved as reusable analytic content.
* **Clear indicators.**\
The UI differentiates **Preview** vs **Full** so you always know the data scope behind a result.
***
### What Iterative is best for
* Rapid exploration when you don’t know the exact question yet
* Multi-turn refinement (e.g., “Explain margin dip → by region → last 6 months → call out anomalies”)
* Quick “sanity-check” views before promoting to a full run
* Multiple concurrent **threads** within a channel (rename, pin, revisit)
***
### Prompting patterns that work
* **Start broad, narrow down:** “Show landed cost by supplier over the last 12 months → add YoY → filter to >$250K purchases → group by category.”
* **Ground definitions:** “Use *landed cost* = base + tariff + freight; treat Tier-1 as ‘priority suppliers’.”
* **Ask for preview sizes:** “Return a **preview table** (10–20 rows) with Supplier, Orders, Landed Cost, Tariff %.”
* **Validate assumptions:** “List the business rules you applied and show the SQL/logic you generated.”
* **Promote when ready:** “Looks good—**run on full data** and summarize in 3 bullets.”
***
### Quality checks before you hit **Approve**
* **Metric definition** matches your business lexicon (e.g., landed cost formula)
* **Filters & time range** are correct (currency/units too)
* **Grouping & sort** reflect how you’ll present the result
* **Preview vs full**—decide if it’s still exploration (keep preview) or ready to **promote to full** / **open in Data Studio**
***
### When to move to Data Studio / GenViz
* You want **production-grade** cards, summary tables, or charts
* You need **full data** across heavy joins, longer time windows, or governed measures/dimensions
* You plan to **reuse** the metric/visual in messages or other channels
***
### Managing threads
* **One topic per thread** to keep the short-term memory clean
* **Rename** meaningful threads, **pin** important ones
* **Clone** a Message to tailor for a different audience without losing the original context
***
### Troubleshooting
* **Answer looks off** → confirm you’re in the right **Channel/Dataverse**; restate key definitions; ask to **show assumptions**
* **No rows returned** → relax filters or extend the date range; request a **wider preview**
* **Slow response** → reduce scope (recent period, fewer dimensions) or stay in preview until ready for full data
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