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PromptLabster

Research-backed prompting techniques from arXiv, rated for practical value

About this launch

Screenshot of the PromptLabster homepage
promptlabster.io, as visitors see it

PromptLabster reads hundreds of AI research papers on arXiv each month and publishes only those with practical prompting techniques that practitioners can apply immediately. Every technique is scored on a 0-100 scale; only papers scoring 80 or higher are published, roughly one in ten papers reviewed. Each technique page includes the method, evidence, measured effects, and ready-to-copy prompts. The site currently catalogs 715 published techniques and sends a weekly newsletter on Sundays with the highest-rated new papers.

What it does

  • 715 published prompting techniques scored 80+ for practical value
  • Scores based on practitioner applicability, evidence strength, effect size, and novelty
  • Copy-paste prompts for ChatGPT, Claude, and Gemini
  • Links to original arXiv papers for every technique
  • Weekly newsletter with top techniques every Sunday
  • Four views per technique: breakdown, plain language, digest, and role-based guides
  • Browsable catalog organized by technique type and publication month
  • No account required to access all pages and techniques
  • Lab score range 80-97 indicates technique quality and applicability

Pricing

Pricing model: Freemium

Shopper questions

What does the lab score measure?

The lab score measures how useful a technique is in practice: whether you can apply it with prompts alone, how strong the evidence is, how large the effect is, and how new the idea is. Papers scoring 80 or higher are published.

Do I need an account to use PromptLabster?

No. Every page is open to read without an account. The newsletter only requires your email address to subscribe.

How often is the site updated?

Every week. About forty new papers a month pass the 80-point threshold. The newsletter brings the best of them to your inbox on Sundays.

Can I trust the numbers on the technique pages?

The numbers are the ones the authors report and describe their models and tasks. Treat them as a strong hint and test the technique on a few of your own examples, as results on your task may differ.

Can I suggest a paper or report a mistake?

Yes. Write to hello@promptlabster.io with the arXiv link, the page, and what looks wrong.

More on this profile

PromptLabster has a free listing. The owner can unlock the full profile with a paid launch for $5:

  • Founder story (locked)
  • Milestones (locked)
  • Demo video (locked)
  • Dofollow website link (locked)

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