--- title: "DecryptPrompt vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dsxiangli-decryptprompt-vs-mlabonne-llm-course" tools: ["dsxiangli-decryptprompt", "mlabonne-llm-course"] --- # DecryptPrompt vs llm-course *GraphCanon updated Sep 20, 2026* ## Verdict Pick DecryptPrompt if decryptPrompt is an open-source repository that summarizes prompt and large language model research papers while offering related datasets and models for AI content generation facilitation; pick llm-course if llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. [DecryptPrompt](https://github.com/DSXiangLi/DecryptPrompt) reports 3.4k GitHub stars, 318 forks, and 0 open issues, last pushed Sep 8, 2026. [llm-course](https://mlabonne.github.io/blog/) has 83k stars, 9.7k forks, and 90 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [DecryptPrompt's repository](https://github.com/DSXiangLi/DecryptPrompt) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [DecryptPrompt](/tools/dsxiangli-decryptprompt.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Summarizes Prompt&LLM Papers, Open-source Data&Models, AIGC Applications | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 3,440 | 83,011 | | Forks | 318 | 9,657 | | Open issues | 0 | 90 | | Language | - | - | | Adopt for | DecryptPrompt is an open-source repository that summarizes prompt and large language model research papers while offering related datasets and models for AI content generation facilitation. | llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Developer Tools, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DecryptPrompt](/tools/dsxiangli-decryptprompt.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 12d | 224d | | Open issues (now) | 0 | 90 | | Stars delta | +13 (30d) | +1.5k (30d) | | Open issues delta | -1 (30d) | +4 (30d) | | Full report | [trust report](/tools/dsxiangli-decryptprompt/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Decision facts: DecryptPrompt - **Adopt for:** DecryptPrompt is an open-source repository that summarizes prompt and large language model research papers while offering related datasets and models for AI content generation facilitation. ## Decision facts: llm-course - **Adopt for:** llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. ## Choose when ### Choose DecryptPrompt if… - Tags unique to DecryptPrompt: aigc, chain-of-thought, chatgpt, demonstration. - When you need detailed summaries of prompt-engineering and LLM-related research, as DecryptPrompt is dedicated to this area. - More recently updated (last pushed Sep 8, 2026). ### Choose llm-course if… - Tags unique to llm-course: course, large-language-models, machine-learning, roadmap. - Also covers Evaluation & Observability, Inference & Serving, LLM Frameworks. - Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment. ## When NOT to use DecryptPrompt - Avoid using DecryptPrompt if you require a solution that supports languages other than English effectively, as the repository's descriptions are in Chinese. - If your development needs go beyond summarization and data/model provision into complex coding examples or comprehensive API documentation, DecryptPrompt might not satisfy these requirements. ## When NOT to use llm-course - Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. - Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. - Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path. ## Common questions ### What is the difference between DecryptPrompt and llm-course? DecryptPrompt: Summarizes Prompt&LLM Papers, Open-source Data&Models, AIGC Applications. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DecryptPrompt over llm-course? Choose DecryptPrompt over llm-course when Tags unique to DecryptPrompt: aigc, chain-of-thought, chatgpt, demonstration; When you need detailed summaries of prompt-engineering and LLM-related research, as DecryptPrompt is dedicated to this area; More recently updated (last pushed Sep 8, 2026). ### When should I choose llm-course over DecryptPrompt? Choose llm-course over DecryptPrompt when Tags unique to llm-course: course, large-language-models, machine-learning, roadmap; Also covers Evaluation & Observability, Inference & Serving, LLM Frameworks; Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment. ### When should I avoid DecryptPrompt? Avoid using DecryptPrompt if you require a solution that supports languages other than English effectively, as the repository's descriptions are in Chinese. If your development needs go beyond summarization and data/model provision into complex coding examples or comprehensive API documentation, DecryptPrompt might not satisfy these requirements. ### When should I avoid llm-course? Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path. ### Is DecryptPrompt or llm-course more popular on GitHub? llm-course has more GitHub stars (83,011 vs 3,440). Stars measure visibility, not whether either tool fits your constraints. ### Are DecryptPrompt and llm-course open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DecryptPrompt or llm-course? GraphCanon lists graph-backed alternatives at [DecryptPrompt alternatives](/tools/dsxiangli-decryptprompt/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([DecryptPrompt markdown twin](/tools/dsxiangli-decryptprompt/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/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/dsxiangli-decryptprompt-vs-mlabonne-llm-course.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DecryptPrompt or llm-course? DecryptPrompt: Active. llm-course: Slowing. 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 DecryptPrompt and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DecryptPrompt trust report](/tools/dsxiangli-decryptprompt/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dsxiangli-decryptprompt`](/api/graphcanon/graph?tool=dsxiangli-decryptprompt) - 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/_