Generate and Auto-Evaluate Facebook Ad Headlines using GPT-4o-mini

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Built by Yaron Been Yaron Been
Created on June 13, 2026

Description

Generate and Auto-Evaluate Facebook Ad Headlines using GPT-4o-mini

Built with n8n + OpenAI

This workflow captures a product description, generates ad headlines, evaluates them with custom criteria, decides whether another draft is needed, and finally sends the best version via Gmail.

⚑ Section 1: Capture the Brief & Build the Prompt

πŸ“ FormTrigger\_CopywritingBrief* β†’ A simple form asks: *β€œWhat is your product about?”
βš™οΈ Set\_PromptForHeadline** β†’ Prepares the input by appending the instruction:
β€œWrite a Facebook ad headline for this product:”

Benefit: Ensures consistent, structured prompts so the AI receives clear context every time.

✍️ Section 2: Draft the Headline

πŸ’¬ LLM\_HeadlineWriterModel** β†’ GPT-4o-mini model provides the intelligence.
✍️ Agent\_HeadlineWriter** β†’ Generates a first-pass Facebook ad headline.

Benefit: Produces creative copy instantly without waiting on a human writer.

πŸ“‹ Section 3: Define Scoring Criteria

πŸ’¬ LLM\_EvalCriteriaModel** β†’ Calls GPT-4o-mini again.
πŸ“‘ Agent\_EvalCriteriaBuilder** β†’ Suggests 5 scoring parameters (scale 1-10).
Example: Clarity, Relevance, Hook Strength, Brand Voice, Scroll-Stoppage.

Benefit: Builds an objective, repeatable evaluation rubric automatically.

πŸ” Section 4: Evaluate the Headline

πŸ’¬ LLM\_HeadlineEvaluatorModel** β†’ Supplies reasoning power.
πŸ” Agent\_HeadlineEvaluator** β†’ Applies the 5 criteria to the generated headline and outputs:

JSON with scores per parameter
An average score
A plain-language bottom-line

Benefit: Turns subjective copy quality into measurable numbers.

πŸ”„ Section 5: Decide & Iterate (if needed)

πŸ’¬ LLM\_BottomLineModel** β†’ Interprets the evaluation results.
πŸ€” Agent\_IterationDecision** β†’ Decides:

Return NO β†’ headline is acceptable.
Return YES + feedback β†’ headline should be rewritten.
πŸ”€ If\_NeedMoreIterations** β†’ Branches:

If NO β†’ continue workflow.
If YES β†’ (loop wiring possible) headline can be regenerated with feedback.

Benefit: Keeps iterating until the AI headline meets your standards.

πŸ“© Section 6: Deliver the Result

πŸ“§ Send a message (Gmail node)** β†’ Sends the accepted headline via email.

Benefit: Automates delivery of the polished, AI-approved headline to your inbox or team.

πŸ“Š Workflow Overview

| Section | Purpose | Key Nodes | Benefit |
| -------------------- | ---------------------------------- | ----------------------------------------------------- | ------------------------------ |
| ⚑ Capture Brief | Collect product info & prep prompt | FormTrigger, Set | Structured AI input |
| ✍️ Draft Headline | Generate first headline | LLM\_HeadlineWriterModel, Agent\_HeadlineWriter | Instant creative draft |
| πŸ“‹ Define Criteria | Build scoring rubric | LLM\_EvalCriteriaModel, Agent\_EvalCriteriaBuilder | Objective evaluation |
| πŸ” Evaluate Headline | Score headline & summarize | LLM\_HeadlineEvaluatorModel, Agent\_HeadlineEvaluator | Transparent quality check |
| πŸ”„ Decide & Iterate | Accept or refine headline | LLM\_BottomLineModel, Agent\_IterationDecision, If | Only good results move forward |
| πŸ“© Deliver Result | Share the final copy | Gmail | Automates delivery |

βœ… Final Benefits

πŸš€ One-click workflow: from product description to tested headline.
πŸ“Š Automatic rubric: objective scoring each time.
πŸ”„ Self-improving: poor headlines can auto-iterate with feedback.
πŸ“§ Direct integration: approved headlines land in Gmail instantly.
🧩 Fully modular: easy to extend with Google Sheets, Slack, or CRM nodes.

Nodes Used (3)

AI Agent
@n8n/n8n-nodes-langchain.agent
Gmail
n8n-nodes-base.gmail
OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi