--- title: "promptsource vs Awesome-AIGC-Tutorials" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigscience-workshop-promptsource-vs-luban-agi-awesome-aigc-tutorials" tools: ["bigscience-workshop-promptsource", "luban-agi-awesome-aigc-tutorials"] --- # promptsource vs Awesome-AIGC-Tutorials *GraphCanon updated Aug 15, 2026* ## Verdict Pick promptsource if promptSource aids in creating, sharing, and using natural language prompts for large language models; pick Awesome-AIGC-Tutorials if awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. [promptsource](https://github.com/bigscience-workshop/promptsource) reports 3.0k GitHub stars, 375 forks, and 43 open issues, last pushed Oct 23, 2023. [Awesome-AIGC-Tutorials](https://github.com/luban-agi/Awesome-AIGC-Tutorials) has 4.5k stars, 303 forks, and 10 open issues, last pushed Mar 31, 2024. Figures are from public GitHub metadata via [promptsource's repository](https://github.com/bigscience-workshop/promptsource) and [Awesome-AIGC-Tutorials's repository](https://github.com/luban-agi/Awesome-AIGC-Tutorials). | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) | | --- | --- | --- | | Tagline | Toolkit for creating, sharing and using natural language prompts | Curated tutorials and resources for Large Language Models, AI Painting, and more | | Stars | 3,029 | 4,522 | | Forks | 375 | 303 | | Open issues | 43 | 10 | | Language | Python | - | | Adopt for | PromptSource aids in creating, sharing, and using natural language prompts for large language models. | Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors. | | Categories | Developer Tools, Model Training | Developer Tools, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) | | --- | --- | --- | | Days since push | 1027d | 848d | | Open issues (now) | 43 | 10 | | Stars delta | +2 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Full report | [trust report](/tools/bigscience-workshop-promptsource/trust.md) | [trust report](/tools/luban-agi-awesome-aigc-tutorials/trust.md) | ## Shared compatibility - **Python**: [promptsource](/tools/bigscience-workshop-promptsource.md) - Python runtime; [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) - Python runtime ## Decision facts: promptsource - **Adopt for:** PromptSource aids in creating, sharing, and using natural language prompts for large language models. ## Decision facts: Awesome-AIGC-Tutorials - **Requirements:** No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial. - **Adopt for:** Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. - **License detail:** MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors. ## Choose when ### Choose promptsource if… - License: promptsource is Apache-2.0, Awesome-AIGC-Tutorials is MIT. - Tags unique to promptsource: few-shot, fine-tuning, language-models, machine-learning. - When you need to create reusable prompts for multiple datasets with a focus on simplicity through a templating language called Jinja. ### Choose Awesome-AIGC-Tutorials if… - License: Awesome-AIGC-Tutorials is MIT, promptsource is Apache-2.0. - Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.. - Tags unique to Awesome-AIGC-Tutorials: ai, aigc, chatgpt, deep-learning. - Also covers LLM Frameworks. - If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources. ## When NOT to use promptsource - Avoid if you require complex prompt customization beyond what simple templating can offer, as PromptSource is not designed for intricate configurations. - Not suitable for users focused on real-time interaction with prompts, since it lacks dynamic features for immediate adjustments. ## When NOT to use Awesome-AIGC-Tutorials - Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples. - Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis. ## Common questions ### What is the difference between promptsource and Awesome-AIGC-Tutorials? promptsource: Toolkit for creating, sharing and using natural language prompts. Awesome-AIGC-Tutorials: Curated tutorials and resources for Large Language Models, AI Painting, and more. See the comparison table for live GitHub stats and shared categories. ### When should I choose promptsource over Awesome-AIGC-Tutorials? Choose promptsource over Awesome-AIGC-Tutorials when License: promptsource is Apache-2.0, Awesome-AIGC-Tutorials is MIT; Tags unique to promptsource: few-shot, fine-tuning, language-models, machine-learning; When you need to create reusable prompts for multiple datasets with a focus on simplicity through a templating language called Jinja. ### When should I choose Awesome-AIGC-Tutorials over promptsource? Choose Awesome-AIGC-Tutorials over promptsource when License: Awesome-AIGC-Tutorials is MIT, promptsource is Apache-2.0; Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.; Tags unique to Awesome-AIGC-Tutorials: ai, aigc, chatgpt, deep-learning; Also covers LLM Frameworks; If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources. ### When should I avoid promptsource? Avoid if you require complex prompt customization beyond what simple templating can offer, as PromptSource is not designed for intricate configurations. Not suitable for users focused on real-time interaction with prompts, since it lacks dynamic features for immediate adjustments. ### When should I avoid Awesome-AIGC-Tutorials? Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples. Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis. ### Is promptsource or Awesome-AIGC-Tutorials more popular on GitHub? Awesome-AIGC-Tutorials has more GitHub stars (4,522 vs 3,029). Stars measure visibility, not whether either tool fits your constraints. ### Are promptsource and Awesome-AIGC-Tutorials open source? Yes - both are open-source projects on GitHub (promptsource: Apache-2.0, Awesome-AIGC-Tutorials: MIT). ### Where can I find alternatives to promptsource or Awesome-AIGC-Tutorials? GraphCanon lists graph-backed alternatives at [promptsource alternatives](/tools/bigscience-workshop-promptsource/alternatives) and [Awesome-AIGC-Tutorials alternatives](/tools/luban-agi-awesome-aigc-tutorials/alternatives) ([promptsource markdown twin](/tools/bigscience-workshop-promptsource/alternatives.md), [Awesome-AIGC-Tutorials markdown twin](/tools/luban-agi-awesome-aigc-tutorials/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/bigscience-workshop-promptsource-vs-luban-agi-awesome-aigc-tutorials.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, promptsource or Awesome-AIGC-Tutorials? promptsource: Dormant. Awesome-AIGC-Tutorials: Dormant. 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 promptsource and Awesome-AIGC-Tutorials? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [promptsource trust report](/tools/bigscience-workshop-promptsource/trust); [Awesome-AIGC-Tutorials trust report](/tools/luban-agi-awesome-aigc-tutorials/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigscience-workshop-promptsource`](/api/graphcanon/graph?tool=bigscience-workshop-promptsource) - 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/_