Comparison
Awesome-Prompt-Engineering vs awesome-LLM-resources
Verdict
Pick Awesome-Prompt-Engineering if awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a.
Markdown twin · Awesome-Prompt-Engineering alternatives · awesome-LLM-resources alternatives
GraphCanon updated 6d
vs
Trust & integrity
| Signal | Awesome-Prompt-Engineering | awesome-LLM-resources |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Very active (2d since push) As of 6d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 6d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- Awesome-Prompt-Engineering
- Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- Awesome-Prompt-Engineering
- 6.2k
- awesome-LLM-resources
- 8.8k
Forks
- Awesome-Prompt-Engineering
- 734
- awesome-LLM-resources
- 950
Open issues
- Awesome-Prompt-Engineering
- 94
- awesome-LLM-resources
- 23
Language
- Awesome-Prompt-Engineering
- TypeScript
- awesome-LLM-resources
- -
Adopt for
- Awesome-Prompt-Engineering
- Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license.
- awesome-LLM-resources
- awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a
Persona
- Awesome-Prompt-Engineering
- -
- awesome-LLM-resources
- -
Runtime
- Awesome-Prompt-Engineering
- -
- awesome-LLM-resources
- -
License
- Awesome-Prompt-Engineering
- Apache-2.0
- awesome-LLM-resources
- Apache-2.0
Last pushed
- Awesome-Prompt-Engineering
- Jul 27, 2026
- awesome-LLM-resources
- Aug 14, 2026
Categories
- Awesome-Prompt-Engineering
- Developer Tools, Model Training
- awesome-LLM-resources
- AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Days since push
- Awesome-Prompt-Engineering
- 0d
- awesome-LLM-resources
- 2d
Open issues (now)
- Awesome-Prompt-Engineering
- 94
- awesome-LLM-resources
- 23
Stars delta
- Awesome-Prompt-Engineering
- Unknown
- awesome-LLM-resources
- +142 (30d)
Open issues delta
- Awesome-Prompt-Engineering
- Unknown
- awesome-LLM-resources
- -13 (30d)
Owner type
- Awesome-Prompt-Engineering
- Organization
- awesome-LLM-resources
- User
Full report
- Awesome-Prompt-Engineering
- Trust report
- awesome-LLM-resources
- Trust report
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
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
Choose awesome-LLM-resources if…
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models.
- Also covers AI Agents, Evaluation & Observability, Inference & Serving, LLM Frameworks.
- - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
When NOT to use awesome-LLM-resources
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage.
- - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (promptslab/Awesome-Prompt-Engineering) · observed Jul 28, 2026
- GitHub forks (promptslab/Awesome-Prompt-Engineering) · observed Jul 28, 2026
- Last push (promptslab/Awesome-Prompt-Engineering) · observed Jul 27, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Aug 14, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 10, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Prompt-Engineering 6.2k · awesome-LLM-resources 8.8k (synced Jul 28, 2026).
Common questions
- What is the difference between Awesome-Prompt-Engineering and awesome-LLM-resources?
- Awesome-Prompt-Engineering: Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Prompt-Engineering over awesome-LLM-resources?
- Choose Awesome-Prompt-Engineering over awesome-LLM-resources 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.
- When should I choose awesome-LLM-resources over Awesome-Prompt-Engineering?
- Choose awesome-LLM-resources over Awesome-Prompt-Engineering when Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Evaluation & Observability, Inference & Serving, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
- 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
- When should I avoid awesome-LLM-resources?
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
- Is Awesome-Prompt-Engineering or awesome-LLM-resources more popular on GitHub?
- awesome-LLM-resources has more GitHub stars (8,845 vs 6,197). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Prompt-Engineering and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub (Awesome-Prompt-Engineering: Apache-2.0, awesome-LLM-resources: Apache-2.0).
- Where can I find alternatives to Awesome-Prompt-Engineering or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at Awesome-Prompt-Engineering alternatives and awesome-LLM-resources alternatives (Awesome-Prompt-Engineering markdown twin, awesome-LLM-resources 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, Awesome-Prompt-Engineering or awesome-LLM-resources?
- Awesome-Prompt-Engineering: Very active. awesome-LLM-resources: 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 Awesome-Prompt-Engineering and awesome-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Prompt-Engineering trust report; awesome-LLM-resources trust report.