--- title: "llm-course vs Awesome-Prompt-Engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-promptslab-awesome-prompt-engineering" tools: ["mlabonne-llm-course", "promptslab-awesome-prompt-engineering"] --- # llm-course vs Awesome-Prompt-Engineering *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-course when requirements: Course materials are available in Colab notebooks; access requires a Google account; pick Awesome-Prompt-Engineering when tags unique to Awesome-Prompt-Engineering: chatgpt, chatgpt-api, deep-learning, few-shot-learning. [llm-course](https://mlabonne.github.io/blog/) reports 81k GitHub stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) has 6.2k stars, 723 forks, and 88 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering). | | [llm-course](/tools/mlabonne-llm-course.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | | Stars | 80,839 | 6,150 | | Forks | 9,421 | 723 | | Open issues | 84 | 88 | | Language | - | TypeScript | | Adopt for | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 155d | 0d | | Open issues (now) | 84 | 88 | | Owner type | User | Organization | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose llm-course if… - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, large language models, roadmap. - Also covers Evaluation & Observability, Inference & Serving. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ### Choose Awesome-Prompt-Engineering if… - Tags unique to Awesome-Prompt-Engineering: chatgpt, chatgpt-api, deep-learning, few-shot-learning. - Also covers Speech & Audio. - More recently updated (last pushed Jul 11, 2026). ## When NOT to use llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## When NOT to use Awesome-Prompt-Engineering - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between llm-course and Awesome-Prompt-Engineering? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over Awesome-Prompt-Engineering? Choose llm-course over Awesome-Prompt-Engineering when Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large language models, roadmap; Also covers Evaluation & Observability, Inference & Serving; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I choose Awesome-Prompt-Engineering over llm-course? Choose Awesome-Prompt-Engineering over llm-course when Tags unique to Awesome-Prompt-Engineering: chatgpt, chatgpt-api, deep-learning, few-shot-learning; Also covers Speech & Audio; More recently updated (last pushed Jul 11, 2026). ### When should I avoid llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### When should I avoid Awesome-Prompt-Engineering? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is llm-course or Awesome-Prompt-Engineering more popular on GitHub? llm-course has more GitHub stars (80,839 vs 6,150). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and Awesome-Prompt-Engineering open source? Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, Awesome-Prompt-Engineering: Apache-2.0). ### Where can I find alternatives to llm-course or Awesome-Prompt-Engineering? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/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/mlabonne-llm-course-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, llm-course or Awesome-Prompt-Engineering? llm-course: Slowing. 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 llm-course and Awesome-Prompt-Engineering? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-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/_