🎮 The Next Input — Issue #015

1

Trigger – Intercom → Conversation Closed.

2

Filter – If tag = faq_candidate, continue.

3

AI Action – Prompt: “Rewrite the agent’s final answer into Q&A format ≤120 words.”

4

Vector Upload – Push Q&A to Pinecone namespace faq_live.

5

Middleware Lambda – Refresh Intercom chatbot retrieval endpoint.

6

Slack Proof – Post draft Q&A to #support-faq; if 👍 reaction in 2 h, keep; else delete vector record.

7

Fail-safe – On vector error, DM #ops-alerts with convo ID.

Context QA

Lightweight RAG micro-service that plugs into any chat widget.

https://contextqa.io

PropelAuth + RAG

Drop-in auth plus tenant-scoped vector search.

https://propelauth.com

Prompt: Two-Line Patch Notes

Paste diff → get release blurb.

prompt below ↓

The Big Idea

Airtable AI Analyst: every new row triggers GPT-4o to benchmark performance against historical data and write a plain-English insight right back into the base.

The Use Case

Customer-feedback mining. Drop thousands of survey lines; the analyst clusters themes, flags sentiment shifts, and highlights the highest-ROI fix.

The Tech Stack

Airtable ⇄ OpenAI via Zapier. Native Automations fetch related records; Zapier handles the GPT-4o call; no extra DB required.

Step-by-Step Build

1) Trigger – Airtable Automation: When record created in “Feedback”. 2) Find related – “Look Up” historical ratings for same feature. 3) Run Script – Send current + historical JSON to GPT-4o. 4) AI Output – GPT returns {cluster, sentiment, insight}. 5) Update Record – Write fields AI Cluster, AI Insight.

Magic Prompt

“You are a data-analyst. Using the current feedback plus the last 100 rows in this cluster, identify trend direction (rising/flat/falling) and write one actionable recommendation in ≤40 words.”

Pro Tip

Chunk your context. Pass only the last ~100 related rows, not the whole base; keeps tokens low and analysis tight. Use the Airtable Script step to slice & dice JSON before the GPT call.