Spike: Release Notes Pipeline and What's New Interaction (not needed)

## Goal

Investigate and document the end-to-end release notes pipeline, from authoring through to the AI-generated "What's New" section, and define the approach for triggering, error handling, and content moderation.

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## Background

The AI-generated "What's New" section has been implemented. This spike focuses on the surrounding pipeline — specifically how release notes flow into the system, when is the what's new flow triggered, how failures are handled, and whether a moderation step is needed. Several open questions must be resolved before a full integration can proceed.

Note: A meeting with Rob and/or Greg has been identified as a prerequisite to unblock several of these questions.

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## Investigation Areas

### 1. Release notes pipeline

- How are release notes currently authored, and in what format?
- Where are they stored — database, repository, or CMS?
- What is the source of truth for release data, and does it differ between staging and production?
- Are release notes available in a consistent, parseable format across all versions, or are there inconsistencies?
### 2. Trigger mechanism

- What event or signal should trigger AI generation?
- Is there a webhook, pipeline step, or manual process that can reliably fire this trigger?
- Should the trigger be automated or manually initiated by a team member?
### 3. Failure handling

- What happens if the AI call fails due to a timeout, API error, or rate limit?
- Define retry logic: how many retries are allowed?
- What is the fallback state? Options to evaluate:
- Show no "What's New" section until generation succeeds, extend the platform table as shown [here](https://www.figma.com/design/5j0fQssrV9ipoU16P7hfKy/Website-Deliverables?node-id=6971-26628&t=gITQ6bCznYzxiI8J-4)
- How are failures surfaced to the team — alerting, logging, or both?

### 4. Content moderation
- No human moderation was planned initially, is that still acceptable?
- If moderation is needed: who reviews, and what is the workflow (approve / reject / edit before publish)?
- If no moderation: what additional guardrails can be placed to prevent hallucinations or inaccurate output? AI counting accuracy was flagged as a concern during refinement session.
- Should staff have an override or regenerate button in the CMS?
- How do we handle a case where the AI output is clearly wrong?
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## Deliverables

- Written summary of the release pipeline, with a diagram if helpful
- Recommended trigger mechanism with pros and cons
- Proposed failure handling strategy covering retry logic and fallback UX
- Moderation recommendation: no-moderation with guardrails vs. lightweight review workflow
- List of any open questions that remain after the investigation
- Ticket breakdown for implementation covering the Release Notes<>AI hand off and retry/error handling
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## Dependencies

- Meeting with Rob and/or Greg to walk through the release pipeline end-to-end
- Access to the release notes data source and database schema
- Validation of any API or pipeline tooling costs if applicable

Start with the required meeting with Rob and/or Greg, then trace the release notes source, storage, and staging/production differences using the available data source and schema access. Done means a written pipeline summary, recommended trigger and failure strategy, moderation recommendation, remaining questions, and implementation ticket breakdown.

Written by the indexing model from the issue text.