awesome-ai-engineering
A curated list of AI engineering resources, roadmaps, tools, and projects.
- Stars
- 22.5k
- Forks
- 4.9k
- Licence
- MIT
- Language
- —
- Last update
- 15 days ago
- Type
- Curated list
What it is
This is a curated list of links to AI engineering material, including learning roadmaps, tools, and example projects. Its description names topics such as LLMs, RAG, AI agents, MCP, fine-tuning, evaluations, and production AI systems. The README supplied for this entry is for a different repository (A to Z Resources for Students), so the contents of this list could not be checked directly.
- Browse the roadmaps to plan a study path in AI engineering
- Find tools and projects on RAG, agents, or evaluations to compare
- Locate learning resources on fine-tuning and production AI systems
Great for
- Developers who want an overview of the AI engineering topics covered in the description
- Students looking for a starting map of LLMs, RAG, and agents
- Engineers who want links to tools and projects to evaluate
- People who prefer reading curated lists to following a single course
Not ideal for
- People looking for installable software: this is a list of links, not a program, and the description does not describe any tool to run
- Readers who need verified, up-to-date guidance: the supplied README does not describe this repository, so its link quality and freshness could not be confirmed
How to get it
- 1
Read it online
Clone the repository to browse it locally
git clone https://github.com/dipakkr/awesome-ai-engineering - 2
Run it
- Open the repository page at https://github.com/dipakkr/awesome-ai-engineering
- Read the README to see the table of contents and section list
- Pick one topic such as RAG or agents and open its linked resources
- Note which links you use and check their dates before relying on them
- 3
Get the most out of it
- Start with the roadmaps if you are new to the field, then move to tools and projects
- Compare several linked projects on the same topic before choosing one
- Check each link's last update date, since a list can outlast the resources it points to
- Use the list as a starting point and confirm claims in the original sources
Downloads
Strengths
- Covers a broad set of current AI engineering topics according to its description
- Released under the MIT license, so the list can be reused with attribution
- Has a large following (22,489 stars at the time of the metadata)
Trade-offs
- The supplied README belongs to another project, so the list's actual contents are unverified
- Curated link lists can go out of date; the last push date alone does not show that individual links still work
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| awesome-ai-engineering (this) | 22.5k | Curated list | beginner | 15 days ago | Developers who want an overview of the AI engineering topics covered in the description |
| ai-engineering-from-scratch | 76.3k | Learning resource | intermediate | 4 days ago | Students who want to understand how LLMs, retrieval, and agents work internally |
| Prompt-Engineering-Guide | 79.8k | Learning resource | beginner | 8 months ago | People who are new to prompting and want structured lessons with examples |
| ai-agents-for-beginners | 78.7k | Learning resource | beginner | 4 days ago | Developers new to AI agents who want guided lessons with code |
| paperclip | 110k | App | advanced | 4 days ago | Developers already running several AI coding agents who want one place to track them |
| hexstrike-ai | 13.3k | AI model / agent | advanced | 3 months ago | Security professionals who want to script or speed up recon with an AI assistant |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.