Comparison
llm-course vs Awesome-Prompt-Engineering
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 Awesome-Prompt-Engineering if awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license.
Markdown twin · llm-course alternatives · Awesome-Prompt-Engineering alternatives
GraphCanon updated Sep 20, 2026
9views this month
vs
Trust & integrity
| Signal | llm-course | Awesome-Prompt-Engineering |
|---|---|---|
| Maintenance | Slowing (224d since push) As of Sep 18, 2026 · github_public_v1 | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- llm-course
- Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
- Awesome-Prompt-Engineering
- Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers
Stars
- llm-course
- 83k
- Awesome-Prompt-Engineering
- 6.3k
Forks
- llm-course
- 9.7k
- Awesome-Prompt-Engineering
- 765
Open issues
- llm-course
- 90
- Awesome-Prompt-Engineering
- 116
Language
- llm-course
- -
- Awesome-Prompt-Engineering
- TypeScript
Adopt for
- llm-course
- llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks.
- Awesome-Prompt-Engineering
- Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license.
Persona
- llm-course
- -
- Awesome-Prompt-Engineering
- -
Runtime
- llm-course
- -
- Awesome-Prompt-Engineering
- -
License
- llm-course
- Apache-2.0
- Awesome-Prompt-Engineering
- Apache-2.0
Last pushed
- llm-course
- Feb 5, 2026
- Awesome-Prompt-Engineering
- Sep 19, 2026
Categories
- llm-course
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- Awesome-Prompt-Engineering
- Developer Tools, Model Training
Trust and health
Maintenance
- llm-course
- Slowing (36%)
- Awesome-Prompt-Engineering
- Very active (96%)
Days since push
- llm-course
- 224d
- Awesome-Prompt-Engineering
- 0d
Open issues (now)
- llm-course
- 90
- Awesome-Prompt-Engineering
- 116
Stars delta
- llm-course
- +1.5k (30d)
- Awesome-Prompt-Engineering
- +139 (30d)
Open issues delta
- llm-course
- +4 (30d)
- Awesome-Prompt-Engineering
- +22 (30d)
Owner type
- llm-course
- User
- Awesome-Prompt-Engineering
- Organization
Full report
- llm-course
- Trust report
- Awesome-Prompt-Engineering
- Trust report
Shared compatibility
- Python · llm-course: Python runtime · Awesome-Prompt-Engineering: Python runtime
Choose llm-course if…
- Tags unique to llm-course: course, large-language-models, llm, 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 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.
Choose Awesome-Prompt-Engineering if…
- Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt.
- You need focused materials on GPT and related models for prompt engineering
- More recently updated (last pushed Sep 19, 2026).
When NOT to use Awesome-Prompt-Engineering
- The project requires languages other than TypeScript
- Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (mlabonne/llm-course) · observed Sep 20, 2026
- GitHub forks (mlabonne/llm-course) · observed Sep 20, 2026
- Last push (mlabonne/llm-course) · observed Feb 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (promptslab/Awesome-Prompt-Engineering) · observed Sep 20, 2026
- GitHub forks (promptslab/Awesome-Prompt-Engineering) · observed Sep 20, 2026
- Last push (promptslab/Awesome-Prompt-Engineering) · observed Sep 19, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: llm-course 83k · Awesome-Prompt-Engineering 6.3k (synced Sep 20, 2026).
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: Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers. 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 Tags unique to llm-course: course, large-language-models, llm, 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 choose Awesome-Prompt-Engineering over llm-course?
- Choose Awesome-Prompt-Engineering over llm-course when Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt; You need focused materials on GPT and related models for prompt engineering; More recently updated (last pushed Sep 19, 2026).
- 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 Awesome-Prompt-Engineering?
- The project requires languages other than TypeScript Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering
- Is llm-course or Awesome-Prompt-Engineering more popular on GitHub?
- llm-course has more GitHub stars (83,011 vs 6,336). 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 and Awesome-Prompt-Engineering alternatives (llm-course markdown twin, Awesome-Prompt-Engineering markdown twin), 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 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; Awesome-Prompt-Engineering trust report.