RAG Works… Until It Doesn’t
The Dark Side of Retrieval-Augmented Generation (RAG)
RAG sounds great, until you try implementing it. Then the cracks start to show.
RAG pulls in irrelevant chunks, mashes together unrelated ideas, and confidently misattributes first-person writing, turning useful context into a confusing mess.
Two Major Issues to Overcome
I ran into two major issues when building my own RAG system:
- Context Blindness — When retrieved chunks don’t carry enough information to be useful.
- First-Person Confusion — When the system doesn’t know who “I” refers to.
Solving the Problems
I’ll show you exactly how I fixed these problems, so your RAG system actually understands what it retrieves.
Conclusion
By the end, you’ll have a 100% local, 100% free, context-aware RAG pipeline running with your preferred local LLM and interface. We’ll also set up an automated knowledge base, so adding new information is frictionless.
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Published via Towards AI
FAQs
* Q: What is RAG?
A: Retrieval-Augmented Generation (RAG) is a type of AI model that uses a combination of natural language processing and machine learning to generate text.
* Q: What are the issues with RAG?
A: RAG can pull in irrelevant chunks, mash together unrelated ideas, and confidently misattribute first-person writing, turning useful context into a confusing mess.
* Q: How do I fix these issues?
A: By implementing context-aware RAG systems and using local LLMs and interfaces, you can overcome the limitations of RAG and create a more accurate and effective writing system.
