SEO Title + Meta Description Generator
Generate 5 title/meta pairs optimized for click-through and keyword coverage.
/ quick answer
Use when publishing a new article and you want variants to A/B or pick from. Feed it the article's primary keyword and a 1-paragraph summary. Generate 5 title/meta pairs optimized for click-through and keyword coverage.
You are an SEO editor. Given a primary keyword and an article summary, return 5 title + meta description pairs.
Rules:
- Title: 50–60 chars, includes primary keyword ideally in the first 40 chars, no clickbait.
- Meta: 140–160 chars, includes primary keyword once, ends with a soft benefit or CTA.
- Vary angle: how-to, comparison, list, definition, opinion.
- Output as a markdown table with columns: Angle | Title (chars) | Meta (chars).
PRIMARY KEYWORD: {{keyword}}
SUMMARY: {{summary}}| Angle | Title | Meta | |---|---|---| | How-to | How to Build a RAG System in 2026 (57) | Step-by-step guide to building production RAG in 2026. Chunking, embeddings, reranking, evals — with real code examples. (156) | | Comparison | RAG vs Fine-Tuning: Which to Pick in 2026 (52) | RAG or fine-tune? Cost, freshness, accuracy and setup compared side-by-side to help you choose the right AI approach today. (159) |
/ frequently asked
What does the SEO Title + Meta Description Generator prompt do?
Use when publishing a new article and you want variants to A/B or pick from. Feed it the article's primary keyword and a 1-paragraph summary.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
How do I use this prompt?
1) Copy the prompt with the copy button. 2) Replace every bracketed variable with your real details. 3) Paste it into your AI assistant. 4) Compare the answer with the example output and ask for changes if needed.
What output should I expect?
| Angle | Title | Meta | |---|---|---| | How-to | How to Build a RAG System in 2026 (57) | Step-by-step guide to building production RAG in 2026. Chunking, embeddings, reranking, evals — with real code examples. (156) | | Comparison | RAG vs Fine-Tuning: Which to Pick in 2026 (52) | RAG or fine-tune.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Prompt Chaining
Pipelining LLM calls where each step's output feeds the next.
- →Few-Shot Prompting
Showing the model 2–5 examples of the task inside the prompt so it mirrors the pattern.
- →System Prompt
A high-priority instruction that sets the model's role, tone and constraints for the whole conversation.
- →Prompt Template
A reusable prompt with named variables filled at runtime.
Related workflows
Turn this into a repeatable process.
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- →Bulk Meta Description Generator
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- →Bulk Product Description Generator
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- →SEO Content Brief Generator
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Related tool stacks
The tools that run it in production.
- →SEO Intelligence Stack
Keyword clustering, SERP analysis, content briefs, and internal linking for authority systems.
Related prompts
Reusable prompts for this job.
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Iterate a weak prompt into a strong one using best practices.
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Generate FAQ Q&As from a page for schema markup.
- →FAQ Block Generator (AEO-Optimized)
Turn any article into 5 crisp FAQ Q&A pairs, formatted for FAQPage schema.
- →JSON Extractor Prompt
Extract structured JSON from any unstructured text.