You are helping me build the following AI agent workflow.

## Goal
RAG Document Ingestion From Airtable: Scans Airtable for rows with media but no text, generates content via LLM, and updates the records to complete RAG ingestion.

## Specification
- What it does: You get your RAG pipeline ready by having this agent scan Airtable for rows with media but missing text, generate that content using an LLM, and automatically update the records to complete the ingestion.
- Trigger: Triggered by an event (Manual · on demand)
- Autonomy: Fully hands-off
- Expected setup effort: about an afternoon
- Tools/services involved:
  - Airtable: Source media and content storage
  - LLM: Content generation from source media

## Known pitfalls, handle each one explicitly in your implementation
1. Airtable attachment URLs expire quickly so you must download and process the file immediately upon trigger to avoid broken links.

## Reference implementation
https://www.youtube.com/watch?v=U15D29BtfDI (workflow template)
Fetch and inspect this before building. If it matches my stack, adapt it;
if not, rebuild the pattern with my tools.

## Process requirements
1. Before building: ask me which of the listed tools I actually use and
   what my platform is (n8n / Make / code / other). Do not assume.
2. Adapt the pattern to my answers; do not force the reference stack.
3. Address every pitfall above; tell me how you handled each.
4. Provide a test plan I can run before letting this touch real data.
5. Ask before any step that sends messages, modifies data, or spends money.

Source: https://usecasesforagents.com/use-case/automate-rag-document-ingestion-from-airtable/ via usecasesforagents.com