--- title: "chatgpt_system_prompt vs Awesome-Prompt-Engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/louisshark-chatgpt-system-prompt-vs-promptslab-awesome-prompt-engineering" tools: ["louisshark-chatgpt-system-prompt", "promptslab-awesome-prompt-engineering"] --- # chatgpt_system_prompt vs Awesome-Prompt-Engineering *GraphCanon updated Jul 28, 2026* ## Verdict Pick chatgpt_system_prompt if chatgpt_system_prompt is an essential resource for developers specifically using GPT models needing a collection of system prompts alongside education on the critical issue of prompt injection and leaking; pick Awesome-Prompt-Engineering if awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. [chatgpt_system_prompt](https://github.com/LouisShark/chatgpt_system_prompt) reports 11k GitHub stars, 1.5k forks, and 1 open issues, last pushed Jul 27, 2026. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) has 6.2k stars, 734 forks, and 94 open issues, last pushed Jul 27, 2026. Figures are from public GitHub metadata via [chatgpt_system_prompt's repository](https://github.com/LouisShark/chatgpt_system_prompt) and [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering). | | [chatgpt_system_prompt](/tools/louisshark-chatgpt-system-prompt.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Tagline | A collection of GPT system prompts and knowledge on prompt injection leaking | Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers | | Stars | 10,699 | 6,197 | | Forks | 1,468 | 734 | | Open issues | 1 | 94 | | Language | HTML | TypeScript | | Adopt for | chatgpt_system_prompt is an essential resource for developers specifically using GPT models needing a collection of system prompts alongside education on the critical issue of prompt injection and leaking. | Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Developer Tools | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [chatgpt_system_prompt](/tools/louisshark-chatgpt-system-prompt.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Open issues (now) | 1 | 94 | | Owner type | User | Organization | | Full report | [trust report](/tools/louisshark-chatgpt-system-prompt/trust.md) | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | ## Decision facts: chatgpt_system_prompt - **Adopt for:** chatgpt_system_prompt is an essential resource for developers specifically using GPT models needing a collection of system prompts alongside education on the critical issue of prompt injection and leaking. ## Decision facts: Awesome-Prompt-Engineering - **Adopt for:** Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. ## Choose when ### Choose chatgpt_system_prompt if… - chatgpt_system_prompt is primarily HTML; Awesome-Prompt-Engineering is TypeScript. - License: chatgpt_system_prompt is MIT, Awesome-Prompt-Engineering is Apache-2.0. - When working with GPT models, you seek direct examples and guidance related to secure prompt design ### Choose Awesome-Prompt-Engineering if… - Awesome-Prompt-Engineering is primarily TypeScript; chatgpt_system_prompt is HTML. - License: Awesome-Prompt-Engineering is Apache-2.0, chatgpt_system_prompt is MIT. - Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, machine-learning. - Also covers Model Training. - You need focused materials on GPT and related models for prompt engineering ## When NOT to use chatgpt_system_prompt - For developing or troubleshooting other types of AI models not based on GPT architecture which might have different requirements for system prompts - In scenarios where the focus is on learning generic machine learning principles rather than specific to GPT's prompt engineering considerations and limitations ## When NOT to use Awesome-Prompt-Engineering - The project requires languages other than TypeScript - Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering ## Common questions ### What is the difference between chatgpt_system_prompt and Awesome-Prompt-Engineering? chatgpt_system_prompt: A collection of GPT system prompts and knowledge on prompt injection leaking. Awesome-Prompt-Engineering: Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers. See the comparison table for live GitHub stats and shared categories. ### When should I choose chatgpt_system_prompt over Awesome-Prompt-Engineering? Choose chatgpt_system_prompt over Awesome-Prompt-Engineering when chatgpt_system_prompt is primarily HTML; Awesome-Prompt-Engineering is TypeScript; License: chatgpt_system_prompt is MIT, Awesome-Prompt-Engineering is Apache-2.0; When working with GPT models, you seek direct examples and guidance related to secure prompt design. ### When should I choose Awesome-Prompt-Engineering over chatgpt_system_prompt? Choose Awesome-Prompt-Engineering over chatgpt_system_prompt when Awesome-Prompt-Engineering is primarily TypeScript; chatgpt_system_prompt is HTML; License: Awesome-Prompt-Engineering is Apache-2.0, chatgpt_system_prompt is MIT; Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, machine-learning; Also covers Model Training; You need focused materials on GPT and related models for prompt engineering. ### When should I avoid chatgpt_system_prompt? For developing or troubleshooting other types of AI models not based on GPT architecture which might have different requirements for system prompts In scenarios where the focus is on learning generic machine learning principles rather than specific to GPT's prompt engineering considerations and limitations ### When should I avoid Awesome-Prompt-Engineering? The project requires languages other than TypeScript Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering ### Is chatgpt_system_prompt or Awesome-Prompt-Engineering more popular on GitHub? chatgpt_system_prompt has more GitHub stars (10,699 vs 6,197). Stars measure visibility, not whether either tool fits your constraints. ### Are chatgpt_system_prompt and Awesome-Prompt-Engineering open source? Yes - both are open-source projects on GitHub (chatgpt_system_prompt: MIT, Awesome-Prompt-Engineering: Apache-2.0). ### Where can I find alternatives to chatgpt_system_prompt or Awesome-Prompt-Engineering? GraphCanon lists graph-backed alternatives at [chatgpt_system_prompt alternatives](/tools/louisshark-chatgpt-system-prompt/alternatives) and [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) ([chatgpt_system_prompt markdown twin](/tools/louisshark-chatgpt-system-prompt/alternatives.md), [Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/louisshark-chatgpt-system-prompt-vs-promptslab-awesome-prompt-engineering.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, chatgpt_system_prompt or Awesome-Prompt-Engineering? chatgpt_system_prompt: Very active. Awesome-Prompt-Engineering: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for chatgpt_system_prompt and Awesome-Prompt-Engineering? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [chatgpt_system_prompt trust report](/tools/louisshark-chatgpt-system-prompt/trust); [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=louisshark-chatgpt-system-prompt`](/api/graphcanon/graph?tool=louisshark-chatgpt-system-prompt) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_