--- title: "llm-course vs Prompt_Engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-nirdiamant-prompt-engineering" tools: ["mlabonne-llm-course", "nirdiamant-prompt-engineering"] --- # llm-course vs Prompt_Engineering *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick Prompt_Engineering if the Prompt_Engineering repository provides hands-on Jupyter Notebook tutorials that guide users through 22 prompt engineering techniques for advanced use of Language Learning Models. [llm-course](https://mlabonne.github.io/blog/) reports 83k GitHub stars, 9.7k forks, and 90 open issues, last pushed Feb 5, 2026. [Prompt_Engineering](https://diamant-ai.com) has 7.9k stars, 1.0k forks, and 9 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [Prompt_Engineering's repository](https://github.com/NirDiamant/Prompt_Engineering). | | [llm-course](/tools/mlabonne-llm-course.md) | [Prompt_Engineering](/tools/nirdiamant-prompt-engineering.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | Hands-on Jupyter Notebook tutorials for prompt engineering with LLMs | | Stars | 83,011 | 7,859 | | Forks | 9,657 | 1,027 | | Open issues | 90 | 9 | | Language | - | Jupyter Notebook | | Adopt for | llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. | The Prompt_Engineering repository provides hands-on Jupyter Notebook tutorials that guide users through 22 prompt engineering techniques for advanced use of Language Learning Models. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [Prompt_Engineering](/tools/nirdiamant-prompt-engineering.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 224d | 1d | | Open issues (now) | 90 | 9 | | Stars delta | +1.5k (30d) | +156 (30d) | | Open issues delta | +4 (30d) | +5 (30d) | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/nirdiamant-prompt-engineering/trust.md) | ## 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. ## Decision facts: Prompt_Engineering - **Adopt for:** The Prompt_Engineering repository provides hands-on Jupyter Notebook tutorials that guide users through 22 prompt engineering techniques for advanced use of Language Learning Models. ## Choose when ### Choose llm-course if… - License: llm-course is Apache-2.0, Prompt_Engineering is Other. - Tags unique to llm-course: course, large-language-models, llm, machine-learning. - Also covers Evaluation & Observability, Inference & Serving, Model Training. - 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. ### Choose Prompt_Engineering if… - License: Prompt_Engineering is Other, llm-course is Apache-2.0. - Tags unique to Prompt_Engineering: ai, chain-of-thought, chatgpt, claude. - When you need practical, step-by-step guidance in Jupyter Notebooks to understand and implement prompt engineering techniques with LLMs. ## 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. ## When NOT to use Prompt_Engineering - If you prefer interactive tooling over manual notebook work, as the repository is heavily based on self-guided Jupyter Notebook exercises. - This repository may not be suitable if you are focused exclusively on specific LLM frameworks like Hugging Face Transformers or SpaCy that it does not emphasize. ## Common questions ### What is the difference between llm-course and Prompt_Engineering? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. Prompt_Engineering: Hands-on Jupyter Notebook tutorials for prompt engineering with LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over Prompt_Engineering? Choose llm-course over Prompt_Engineering when License: llm-course is Apache-2.0, Prompt_Engineering is Other; Tags unique to llm-course: course, large-language-models, llm, machine-learning; Also covers Evaluation & Observability, Inference & Serving, Model Training; 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 choose Prompt_Engineering over llm-course? Choose Prompt_Engineering over llm-course when License: Prompt_Engineering is Other, llm-course is Apache-2.0; Tags unique to Prompt_Engineering: ai, chain-of-thought, chatgpt, claude; When you need practical, step-by-step guidance in Jupyter Notebooks to understand and implement prompt engineering techniques with LLMs. ### 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. ### When should I avoid Prompt_Engineering? If you prefer interactive tooling over manual notebook work, as the repository is heavily based on self-guided Jupyter Notebook exercises. This repository may not be suitable if you are focused exclusively on specific LLM frameworks like Hugging Face Transformers or SpaCy that it does not emphasize. ### Is llm-course or Prompt_Engineering more popular on GitHub? llm-course has more GitHub stars (83,011 vs 7,859). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and Prompt_Engineering open source? Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, Prompt_Engineering: Other). ### Where can I find alternatives to llm-course or Prompt_Engineering? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [Prompt_Engineering alternatives](/tools/nirdiamant-prompt-engineering/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [Prompt_Engineering markdown twin](/tools/nirdiamant-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/mlabonne-llm-course-vs-nirdiamant-prompt-engineering.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-course or Prompt_Engineering? llm-course: Slowing. 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 llm-course and Prompt_Engineering? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [Prompt_Engineering trust report](/tools/nirdiamant-prompt-engineering/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mlabonne-llm-course`](/api/graphcanon/graph?tool=mlabonne-llm-course) - 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/_