docs/release_notes_2026_06.md

# DemoPrep June 2026 Release Notes

This is the working prod-update summary for the recent DemoPrep changes. It is
intended to be updated as fixes are tested and promoted.

## Branch / Deploy State

- Current reconciled code line: `develop` / `new-branch`
- Test deploy target: `hf-test/main`
- GitHub source target: `origin/develop`
- Last known reconciled commit before the current local fixes: `d58f32a`

## Data Quality And Dataset Generation

- Moved the app toward dataset-first generation:
  - research and scenario context produce a scenario contract
  - the dataset is generated coherently first
  - DDL is derived from the generated dataset
  - Snowflake loads the generated dataset directly
- Added scenario-family routing instead of one-off company-specific generators.
- Added and improved specialized or richer dataset paths for:
  - SaaS sales
  - retail sales
  - professional services
  - trucking/shipping/logistics
  - banking marketing and finance
  - restaurant/store operations
  - education enrollment
  - healthcare/life sciences
  - CPG/grocery finance and sales
- Improved data realism:
  - better dimension cardinality so datasets do not look toy-sized
  - coherent formulas and constraints for rates, funnels, margins, passengers,
    operations, and finance metrics
  - fewer random placeholder values and fewer impossible metric combinations
- Improved generated product, warehouse, and dimension names to reduce generic
  numbered labels such as `Product 29` where possible.
- Added quality-run tooling and reporting:
  - e2e quality runs save JSON/Markdown artifacts
  - results can be written to `ts_quality_results`
  - the ThoughtSpot quality reporting liveboard reads from `ts_quality_results`
    rather than `session_logs`

## Custom Context And Scenario Specificity

- Custom prompts and additional context are passed into scenario extraction so
  custom demos should preserve customer/domain nouns rather than falling back to
  generic matrix defaults.
- Scenario extraction is intended to drive public/custom demos; deterministic
  routing is a fallback and guardrail, not the main intelligence layer.
- Fixed cases where industry/use-case combinations routed to the wrong scenario
  family, including several logistics, finance, and CPG paths.

## ThoughtSpot Deployment Reliability

- Chunked ThoughtSpot table imports to reduce gateway timeouts on larger TML
  imports.
- Fixed import-scope issues in the chunked deploy path.
- Added recovery behavior for ThoughtSpot partial table creation:
  - after 502/503/504 import errors, the deployer checks whether ThoughtSpot
    actually created the table
  - if the table exists for the same database/schema/connection, the deployer
    reuses the table GUID instead of failing on `table already exists`
- Batch 2 join-update failures are now treated as warnings when the run can
  continue to create a useful model/liveboard.
- Partial liveboard failures are handled more explicitly so a created dataset
  and model are still shown to the user.

## Logging, Run History, And Diagnostics

- Reworked session logging away from shared/module-level state so concurrent or
  repeated runs are less likely to contaminate each other.
- Session IDs are collision-resistant and include microseconds/random suffixes.
- Added terminal run events:
  - `run completed`
  - `run failed`
  - `run waiting for user`
  - `run interrupted`
- Run History now distinguishes:
  - `Success`
  - `Failed`
  - `Partial Success`
  - `Waiting for User`
  - `Interrupted`
  - `Stale / Interrupted`
  - `No Run Started`
- Run History now hides `testrunner@thoughtspot.com` by default and has a
  `Show test runs` toggle.
- Added clearer diagnostics when Snowflake deploy completes but the app does not
  receive the auto-ThoughtSpot handoff.
- Admin `LOG_LEVEL` controls logging:
  - `off`: no Supabase session logging
  - `regular`: important stage and terminal events
  - `verbose`: detailed sub-step logs for incident/debug windows

## LLM Model Handling

- Fixed Anthropic requests for models that reject deprecated `temperature`.
- Temporarily forced or defaulted working model paths during provider quota/rate
  incidents, especially around Sonnet model availability.
- Ensured selected LLM model flows into downstream semantic/model enrichment
  calls instead of silently using inconsistent defaults.
- Logged model resolution at run start so provider/model issues can be diagnosed
  from `session_logs`.

## Onboarding And Admin

- Added temporary-password onboarding flow.
- Admin can add users and generate a Slack-ready invite message.
- New users can be forced to change password before accessing the app.
- Removed the unexpected front-end password length requirement.
- Improved onboarding invite copy:
  - welcome message
  - app location
  - docs/quick-start link
  - username and temporary password
  - contact Mike Boone for problems
- Default new-user settings:
  - ThoughtSpot environment: SE Cloud Primary
  - data size: Medium
  - column naming style: Regular Case
  - tag/object prefix/share defaults empty

## Test Harness And Deployment Workflow

- The e2e quality harness now resolves schemas from the exact ThoughtSpot model
  rather than guessing from prefixes or timestamps.
- The harness can reconcile late-completing runs via `session_logs`.
- Added dataset-first fixed suites and broader randomized/customer-style runs.
- Reconciled GitHub `origin/develop` and Hugging Face `hf-test/main` so test and
  source control are on one code line.
- Documented deploy rule:
  - test: `git push hf-test develop:main`
  - production only when explicitly requested

## Open Follow-Ups

- Date modeling: decide whether most generated models should stop creating
  noisy physical `DATES` tables and rely on fact-table date columns plus
  ThoughtSpot date intelligence instead.
- Liveboards: data scores improved faster than liveboard quality. A separate
  liveboard redesign effort is in progress.
- ThoughtSpot deployment: continue monitoring 504 and version-conflict behavior
  under real customer runs.
- Run History: validate the new statuses against real prod/test logs after the
  next deploy.