Comparison
llm-course vs Awesome-Prompt-Engineering
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.
Markdown twin · llm-course alternatives · Awesome-Prompt-Engineering alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | llm-course | Awesome-Prompt-Engineering |
|---|---|---|
| Maintenance | Slowing (155d since push) As of 1d · github_public_v1 | Very active (0d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1d · github_public_v1 | Not a fork · Organization account As of 1d · github_public_v1 |
| Security (OSV) | No lockfile As of 1d · none | No lockfile As of 1d · none |
Tagline
- 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
Stars
- llm-course
- 81k
- Awesome-Prompt-Engineering
- 6.2k
Forks
- llm-course
- 9.4k
- Awesome-Prompt-Engineering
- 723
Open issues
- llm-course
- 84
- Awesome-Prompt-Engineering
- 88
Language
- llm-course
- -
- Awesome-Prompt-Engineering
- TypeScript
Adopt for
- llm-course
- 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
- Awesome-Prompt-Engineering
- -
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
- Jul 11, 2026
Categories
- llm-course
- Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- Awesome-Prompt-Engineering
- LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- llm-course
- Slowing (36%)
- Awesome-Prompt-Engineering
- Very active (96%)
Days since push
- llm-course
- 155d
- Awesome-Prompt-Engineering
- 0d
Open issues (now)
- llm-course
- 84
- Awesome-Prompt-Engineering
- 88
Owner type
- llm-course
- User
- Awesome-Prompt-Engineering
- Organization
Full report
- llm-course
- Trust report
- Awesome-Prompt-Engineering
- Trust report
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 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
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 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.
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 Jul 11, 2026
- GitHub forks (mlabonne/llm-course) · observed Jul 11, 2026
- Last push (mlabonne/llm-course) · observed Feb 5, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (promptslab/Awesome-Prompt-Engineering) · observed Jul 11, 2026
- GitHub forks (promptslab/Awesome-Prompt-Engineering) · observed Jul 11, 2026
- Last push (promptslab/Awesome-Prompt-Engineering) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: llm-course 81k · Awesome-Prompt-Engineering 6.2k (synced Jul 11, 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: 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 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.