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AI Engineering Hub: Working Examples for LLMs, RAGs, and Agents
A GitHub collection that bundles tutorials on real patterns instead of theory. Useful if the gaps it fills match your stack.
patchy631/ai-engineering-hub ↗This is a repository of working tutorials focused on three things builders actually need to ship: large language models (how to call them and handle responses), RAG systems (retrieval-augmented generation, or how to feed an LLM your own data so it answers specific questions), and AI agents (systems that make decisions and take actions autonomously). Instead of scattered Medium posts and documentation, these tutorials live in one place with code you can run. The value is time saved on research, but only if the tutorials go deep enough on your actual problem. Skim the table of contents first; breadth doesn't matter if the depth misses your use case.
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ai-engineering-hub: LLM + RAG + agents in one repo
Practical tutorials on working with language models, retrieval-augmented generation, and autonomous agents. Bundles real patterns instead of scattered examples. Worth a look if you're shipping AI features and tired of doc-hopping. Check the index first to see if it covers your actual workflow.
ai-engineering-hub bundles working tutorials on LLMs, RAG systems, and autonomous agents in one place. Saves the research tax if you're building real features. Not revolutionary, but saves time. https://github.com/patchy631/ai-engineering-hub
Building with LLMs, RAG, or AI agents. If you're tired of hunting scattered tutorials, this repo consolidates working examples in one place. Pattern-focused, not hype-focused. Concrete code you can learn from and adapt. Worth bookmarking if any of those three stacks are in your build. https://github.com/patchy631/ai-engineering-hub
Just found a resource that actually fills the gap I kept hitting: LLMs, RAGs, and agents covered end-to-end with working code. Most AI tutorials pick one pattern and call it a day. This one goes deeper. The tutorials walk through real-world applications, not toy examples. If you're building with LLMs and tired of stitching together five different sources to understand how RAG actually integrates with agents, this saves the legwork. Worth bookmarking if you're shipping AI features this quarter.
found a github repo that actually connects the dots on LLMs + RAG + agents. most tutorials stop at theory. these go straight to working implementations. if you're building ai features and keep context-switching between docs, this consolidates it. worth a star.