--- title: "promptsource vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigscience-workshop-promptsource-vs-mlabonne-llm-course" tools: ["bigscience-workshop-promptsource", "mlabonne-llm-course"] --- # promptsource vs llm-course *GraphCanon updated Jul 12, 2026* ## Verdict Pick promptsource when tags unique to promptsource: natural-language-processing, nlp, python; pick llm-course when requirements: Course materials are available in Colab notebooks; access requires a Google account. [promptsource](https://github.com/bigscience-workshop/promptsource) reports 3.0k GitHub stars, 377 forks, and 43 open issues, last pushed Oct 23, 2023. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [promptsource's repository](https://github.com/bigscience-workshop/promptsource) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Toolkit for creating, sharing and using natural language prompts. | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 3,026 | 80,839 | | Forks | 377 | 9,421 | | Open issues | 43 | 84 | | Language | Python | - | | 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 | Developer Tools, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 991d | 155d | | Open issues (now) | 43 | 84 | | Owner type | Organization | User | | Full report | [trust report](/tools/bigscience-workshop-promptsource/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Shared compatibility - **Python**: [promptsource](/tools/bigscience-workshop-promptsource.md) - Python runtime; [llm-course](/tools/mlabonne-llm-course.md) - Python runtime ## 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 promptsource if… - Tags unique to promptsource: natural-language-processing, nlp, python. - Also covers Developer Tools. - Leaner open-issue backlog (43). ### 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 ## When NOT to use promptsource - Last GitHub push was 992 days ago (dormant maintenance, Oct 23, 2023). Validate activity before betting a new project on promptsource. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - 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. ## 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 ## Common questions ### What is the difference between promptsource and llm-course? promptsource: Toolkit for creating, sharing and using natural language prompts.. 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 promptsource over llm-course? Choose promptsource over llm-course when Tags unique to promptsource: natural-language-processing, nlp, python; Also covers Developer Tools; Leaner open-issue backlog (43). ### When should I choose llm-course over promptsource? Choose llm-course over promptsource 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 avoid promptsource? Last GitHub push was 992 days ago (dormant maintenance, Oct 23, 2023). Validate activity before betting a new project on promptsource. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. 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. ### 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 ### Is promptsource or llm-course more popular on GitHub? llm-course has more GitHub stars (80,839 vs 3,026). Stars measure visibility, not whether either tool fits your constraints. ### Are promptsource and llm-course open source? Yes - both are open-source projects on GitHub (promptsource: Apache-2.0, llm-course: Apache-2.0). ### Where can I find alternatives to promptsource or llm-course? GraphCanon lists graph-backed alternatives at [promptsource alternatives](/tools/bigscience-workshop-promptsource/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([promptsource markdown twin](/tools/bigscience-workshop-promptsource/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/bigscience-workshop-promptsource-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, promptsource or llm-course? promptsource: Dormant. 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 promptsource and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [promptsource trust report](/tools/bigscience-workshop-promptsource/trust); [llm-course trust report](/tools/mlabonne-llm-course/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/_