Comparison
awesome-prompts vs ai-engineering-hub
Verdict
Pick awesome-prompts when license: awesome-prompts is GPL-3.0, ai-engineering-hub is MIT; pick ai-engineering-hub when license: ai-engineering-hub is MIT, awesome-prompts is GPL-3.0.
Markdown twin · awesome-prompts alternatives · ai-engineering-hub alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | awesome-prompts | ai-engineering-hub |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Steady (32d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of 1d · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No MCP manifest As of 1d · mcp_manifest |
Tagline
- awesome-prompts
- Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.
- ai-engineering-hub
- Tutorials on LLMs, RAGs, and real-world AI agent applications
Stars
- awesome-prompts
- 8.4k
- ai-engineering-hub
- 36k
Forks
- awesome-prompts
- 798
- ai-engineering-hub
- 6.0k
Open issues
- awesome-prompts
- 35
- ai-engineering-hub
- 119
Language
- awesome-prompts
- -
- ai-engineering-hub
- Jupyter Notebook
Adopt for
- awesome-prompts
- -
- ai-engineering-hub
- A collection of in-depth tutorials aiming to cover a wide range from beginner to advanced concepts in AI, including large language models (LLMs), Retrieval-Augmented Generation (RAG) systems and practical applications of
Persona
- awesome-prompts
- -
- ai-engineering-hub
- -
Runtime
- awesome-prompts
- -
- ai-engineering-hub
- -
License
- awesome-prompts
- GPL-3.0
- ai-engineering-hub
- MIT License
Last pushed
- awesome-prompts
- Jul 11, 2026
- ai-engineering-hub
- Jun 8, 2026
Categories
- awesome-prompts
- LLM Frameworks
- ai-engineering-hub
- AI Agents, LLM Frameworks
Trust and health
Maintenance
- awesome-prompts
- Very active (96%)
- ai-engineering-hub
- Steady (60%)
Days since push
- awesome-prompts
- 0d
- ai-engineering-hub
- 32d
Open issues (now)
- awesome-prompts
- 35
- ai-engineering-hub
- 119
Security scan
- awesome-prompts
- No lockfile
- ai-engineering-hub
- No MCP manifest
Full report
- awesome-prompts
- Trust report
- ai-engineering-hub
- Trust report
Choose awesome-prompts if…
- License: awesome-prompts is GPL-3.0, ai-engineering-hub is MIT.
- Tags unique to awesome-prompts: awesome, awesome-list, chatgpt, gpt4.
- More recently updated (last pushed Jul 11, 2026).
When NOT to use awesome-prompts
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Choose ai-engineering-hub if…
- License: ai-engineering-hub is MIT, awesome-prompts is GPL-3.0.
- Requirements: The tutorials and projects use Jupyter Notebooks which require Python and a compatible local environment or cloud-based Jupyter services..
- Tags unique to ai-engineering-hub: agents, ai, llms, machine-learning.
- Also covers AI Agents.
- When you are looking for comprehensive learning paths ranging from complete beginners to advanced experts.
When NOT to use ai-engineering-hub
- If your team already has significant proficiency in AI engineering and advanced LLM frameworks, as the content starts from zero knowledge up.
- When you specifically need industry-standard proprietary tools or heavily specialized niche applications that go beyond foundational learning covered by this hub.
- In scenarios where immediate advanced project results are required; ai-engineering-hub focuses on education through step-by-step tutorials rather than providing ready-made solutions with minimal setup
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ai-boost/awesome-prompts) · observed Jul 11, 2026
- GitHub forks (ai-boost/awesome-prompts) · observed Jul 11, 2026
- Last push (ai-boost/awesome-prompts) · observed Jul 11, 2026
- License file (GPL-3.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (patchy631/ai-engineering-hub) · observed Jul 11, 2026
- GitHub forks (patchy631/ai-engineering-hub) · observed Jul 11, 2026
- Last push (patchy631/ai-engineering-hub) · observed Jun 8, 2026
- License file (MIT) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-prompts 8.4k · ai-engineering-hub 36k (synced Jul 11, 2026).
Common questions
- What is the difference between awesome-prompts and ai-engineering-hub?
- awesome-prompts: Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.. ai-engineering-hub: Tutorials on LLMs, RAGs, and real-world AI agent applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-prompts over ai-engineering-hub?
- Choose awesome-prompts over ai-engineering-hub when License: awesome-prompts is GPL-3.0, ai-engineering-hub is MIT; Tags unique to awesome-prompts: awesome, awesome-list, chatgpt, gpt4; More recently updated (last pushed Jul 11, 2026).
- When should I choose ai-engineering-hub over awesome-prompts?
- Choose ai-engineering-hub over awesome-prompts when License: ai-engineering-hub is MIT, awesome-prompts is GPL-3.0; Requirements: The tutorials and projects use Jupyter Notebooks which require Python and a compatible local environment or cloud-based Jupyter services.; Tags unique to ai-engineering-hub: agents, ai, llms, machine-learning; Also covers AI Agents; When you are looking for comprehensive learning paths ranging from complete beginners to advanced experts.
- When should I avoid awesome-prompts?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- When should I avoid ai-engineering-hub?
- If your team already has significant proficiency in AI engineering and advanced LLM frameworks, as the content starts from zero knowledge up. When you specifically need industry-standard proprietary tools or heavily specialized niche applications that go beyond foundational learning covered by this hub. In scenarios where immediate advanced project results are required; ai-engineering-hub focuses on education through step-by-step tutorials rather than providing ready-made solutions with minimal setup
- Is awesome-prompts or ai-engineering-hub more popular on GitHub?
- ai-engineering-hub has more GitHub stars (36,439 vs 8,440). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-prompts and ai-engineering-hub open source?
- Yes - both are open-source projects on GitHub (awesome-prompts: GPL-3.0, ai-engineering-hub: MIT).
- Where can I find alternatives to awesome-prompts or ai-engineering-hub?
- GraphCanon lists graph-backed alternatives at awesome-prompts alternatives and ai-engineering-hub alternatives (awesome-prompts markdown twin, ai-engineering-hub 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-prompts or ai-engineering-hub?
- awesome-prompts: Very active. ai-engineering-hub: Steady. 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-prompts and ai-engineering-hub?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-prompts trust report; ai-engineering-hub trust report.