Free
$0Free plan available.
Promptstacks is a community-driven platform where users can discover and share Generative AI tips and tricks. It offers vetted prompts for large language models like ChatGPT, delivered through prompt engineering. The platform also features a community where users can view top-voted prompts, submit their own, and join a Discord server for collaboration and discussion.
Users can sign up to join the community, browse articles and courses, access vetted prompts, submit their own prompts, participate in discussions, and access resources related to AI and prompt engineering. They can also join the Discord server for real-time collaboration.
Promptstacks is a community-driven platform where users can share and discover Generative AI tips and tricks. Members can also discuss prompt engineering and general industry news.
The goal of prompt engineering is to generate more relevant, coherent, or accurate output from large language models such as ChatGPT or Bard.
Yes, the community is free to join. You can interact with others, discuss prompt engineering, and share tips and tricks at no cost.
Our courses are curated from industry experts and include engaging videos, prompts, and opportunities to interact with the course instructor. While these comprehensive courses are not free, they provide extensive knowledge and resources to help you master the subject matter.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Promptstacks.
Compare GPT 5.4 and GPT 5.4 Pro across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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Compare GPT 5.5 and Claude 4.6 Opus across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Sonnet across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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