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) |
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.
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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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.
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