Context
NovaOps uses Airtable to manage marketing and sales records. Users want to select thousands of rows and bulk-generate fields such as product descriptions, outreach drafts, and SEO summaries using an external LLM provider, but Airtable interactions must remain responsive.
Constraints
- Main Airtable UI/API writes must stay responsive: synchronous user action should return in under 300ms
- Bulk jobs may process up to 100,000 rows per run
- Target generation latency: p50 under 8s per row end-to-end in the async pipeline, p95 under 20s
- Cost ceiling: $12 per 1,000 generated rows on average
- Hallucination / unsupported-claim rate: under 2% on a labeled offline set for fields requiring factual grounding
- Prompt injection success rate from row data or retrieved context: under 0.5%
- Must support retries, idempotency, partial failures, and provider rate limits
- Generated content must be written back in a structured, schema-safe way
Available Resources
- Airtable base with row data, attachments, and formula fields
- Optional knowledge sources: brand guidelines, product catalog, approved messaging docs, and prior approved examples
- External LLM provider (OpenAI or Anthropic), embeddings model, and a queue/worker system
- Historical human-edited outputs for ~50,000 rows across several content types
- A small review team that can label 500-1,000 examples for evaluation
Task
- Design the end-to-end architecture for bulk generation, including job submission, async workers, write-back, retries, and keeping Airtable responsive.
- Specify the prompt and output schema for generating safe, structured content across multiple row types.
- Define an evaluation plan first: offline quality, hallucination, and prompt-injection testing, plus online monitoring and rollout metrics.
- Estimate cost and latency at 1M generated rows per month, and explain how you would stay within budget.
- Identify key failure modes, including bad row data, provider outages, duplicate writes, and unsafe generations, with mitigations.
