CONCLUSION

You've reached the end. If you read through everything without building the projects, you've wasted your time - reading about systems isn't the same as building them. Go back and actually code something.

If you built the projects and struggled through the debugging, congratulations. You now have more practical knowledge about production RAG + KG systems than most people talking about "AI engineering" on LinkedIn. You can:

• Build production-grade RAG systems (not demos)
• Design and query knowledge graphs (not toy examples)
• Create hybrid RAG + KG architectures (and know when not to)
• Deploy enterprise-ready AI applications (with proper monitoring and cost controls)
• Evaluate and optimize retrieval systems (with actual metrics)

What Happens Next:

The material here doesn't expire next week. RAG and knowledge graphs will still be relevant in 5 years, even as specific models and tools change. The fundamentals don't change.

If you want to get hired, build something real and put it on GitHub. Write about what you learned and what broke. Nobody cares about course completion certificates - they care about shipped code and lessons learned.

The field evolves fast. New models, new databases, new techniques. That's not an excuse to wait - it's a reason to start now and iterate.

Now go build something.

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Course Version: 1.0 (2026) Last Updated: December 2026 License: Educational Use

For questions, updates, or feedback: [Create an issue on GitHub or reach out via LinkedIn]

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