--- title: "awesome-generative-ai-guide vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aishwaryanr-awesome-generative-ai-guide-vs-rucaibox-llmsurvey" tools: ["aishwaryanr-awesome-generative-ai-guide", "rucaibox-llmsurvey"] --- # awesome-generative-ai-guide vs LLMSurvey *GraphCanon updated Sep 20, 2026* ## Verdict Pick awesome-generative-ai-guide if awesome-generative-ai-guide is a curated repository for generative AI resources, including research updates, interview questions, and Jupyter notebooks, aimed at researchers and developers in the field of generative AI; pick LLMSurvey if lLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long. [awesome-generative-ai-guide](https://www.linkedin.com/in/areganti/) reports 29k GitHub stars, 6.0k forks, and 4 open issues, last pushed Sep 17, 2026. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 930 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [awesome-generative-ai-guide's repository](https://github.com/aishwaryanr/awesome-generative-ai-guide) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [awesome-generative-ai-guide](/tools/aishwaryanr-awesome-generative-ai-guide.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | A one stop repository for generative AI research updates, interview resources, notebooks and much more! | A collection of papers and resources related to Large Language Models | | Stars | 29,463 | 12,216 | | Forks | 5,953 | 930 | | Open issues | 4 | 30 | | Language | HTML | Python | | Adopt for | awesome-generative-ai-guide is a curated repository for generative AI resources, including research updates, interview questions, and Jupyter notebooks, aimed at researchers and developers in the field of generative AI. | LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. | | Persona | - | - | | Runtime | - | - | | License | MIT | The license information for LLMSurvey is unknown. | | Categories | Computer Vision, Developer Tools, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-generative-ai-guide](/tools/aishwaryanr-awesome-generative-ai-guide.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 555d | | Open issues (now) | 4 | 30 | | Stars delta | +692 (30d) | +11 (30d) | | Open issues delta | -1 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/aishwaryanr-awesome-generative-ai-guide/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) | ## Decision facts: awesome-generative-ai-guide - **Adopt for:** awesome-generative-ai-guide is a curated repository for generative AI resources, including research updates, interview questions, and Jupyter notebooks, aimed at researchers and developers in the field of generative AI. ## Decision facts: LLMSurvey - **Pricing:** freemium - The repository is free to access, but the Chinese book is available for purchase. - **Requirements:** Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book. - **Adopt for:** LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. - **License detail:** The license information for LLMSurvey is unknown. ## Choose when ### Choose awesome-generative-ai-guide if… - awesome-generative-ai-guide is primarily HTML; LLMSurvey is Python. - Tags unique to awesome-generative-ai-guide: awesome, awesome-list, generative-ai, interview-questions. - Also covers Computer Vision, Developer Tools. - Use awesome-generative-ai-guide when you need a comprehensive collection of resources for generative AI research, including updates and interview preparation materials. ### Choose LLMSurvey if… - LLMSurvey is primarily Python; awesome-generative-ai-guide is HTML. - Pricing: The repository is free to access, but the Chinese book is available for purchase.. - Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.. - Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning. - Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ## When NOT to use awesome-generative-ai-guide - Do not use awesome-generative-ai-guide if you are seeking a platform for real-time collaboration on AI projects, as it primarily serves as a repository of static resources. - Avoid using this tool if you require a more specialized focus on a particular aspect of AI, such as reinforcement learning or natural language processing, as it is a broad overview and may not cover a ## When NOT to use LLMSurvey - Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. - Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ## Common questions ### What is the difference between awesome-generative-ai-guide and LLMSurvey? awesome-generative-ai-guide: A one stop repository for generative AI research updates, interview resources, notebooks and much more!. LLMSurvey: A collection of papers and resources related to Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-generative-ai-guide over LLMSurvey? Choose awesome-generative-ai-guide over LLMSurvey when awesome-generative-ai-guide is primarily HTML; LLMSurvey is Python; Tags unique to awesome-generative-ai-guide: awesome, awesome-list, generative-ai, interview-questions; Also covers Computer Vision, Developer Tools; Use awesome-generative-ai-guide when you need a comprehensive collection of resources for generative AI research, including updates and interview preparation materials. ### When should I choose LLMSurvey over awesome-generative-ai-guide? Choose LLMSurvey over awesome-generative-ai-guide when LLMSurvey is primarily Python; awesome-generative-ai-guide is HTML; Pricing: The repository is free to access, but the Chinese book is available for purchase.; Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.; Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning; Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ### When should I avoid awesome-generative-ai-guide? Do not use awesome-generative-ai-guide if you are seeking a platform for real-time collaboration on AI projects, as it primarily serves as a repository of static resources. Avoid using this tool if you require a more specialized focus on a particular aspect of AI, such as reinforcement learning or natural language processing, as it is a broad overview and may not cover a ### When should I avoid LLMSurvey? Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ### Is awesome-generative-ai-guide or LLMSurvey more popular on GitHub? awesome-generative-ai-guide has more GitHub stars (29,463 vs 12,216). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-generative-ai-guide and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-generative-ai-guide or LLMSurvey? GraphCanon lists graph-backed alternatives at [awesome-generative-ai-guide alternatives](/tools/aishwaryanr-awesome-generative-ai-guide/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([awesome-generative-ai-guide markdown twin](/tools/aishwaryanr-awesome-generative-ai-guide/alternatives.md), [LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/aishwaryanr-awesome-generative-ai-guide-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-generative-ai-guide or LLMSurvey? awesome-generative-ai-guide: Very active. LLMSurvey: 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 awesome-generative-ai-guide and LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-generative-ai-guide trust report](/tools/aishwaryanr-awesome-generative-ai-guide/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aishwaryanr-awesome-generative-ai-guide`](/api/graphcanon/graph?tool=aishwaryanr-awesome-generative-ai-guide) - 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/_