Your AI Is Showing — Post 9
📰 Medium · RAG
Learn about the weaknesses of vector and embedding in LLMs and how RAG can be affected by updates to the knowledge base
Action Steps
- Identify potential weaknesses in your LLM's vector and embedding techniques
- Update your knowledge base with new documents and retrain your model
- Test the performance of your RAG model after updating the knowledge base
- Analyze the impact of the updates on the model's accuracy and adjust accordingly
- Configure your model to handle updates to the knowledge base more efficiently
Who Needs to Know This
Data scientists and AI engineers working with LLMs and RAG can benefit from understanding the limitations of vector and embedding techniques, especially when the knowledge base is updated
Key Insight
💡 Vector and embedding weaknesses in LLMs can be exposed when the knowledge base is updated, affecting RAG performance
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🚨 Your AI is showing its weaknesses! 🚨 Learn about vector & embedding limitations in LLMs and how RAG is affected by knowledge base updates
Key Takeaways
Learn about the weaknesses of vector and embedding in LLMs and how RAG can be affected by updates to the knowledge base
Full Article
LLM08: Vector & Embedding Weaknesses — RAG is powerful. But someone added a document to your knowledge base last week. Continue reading on Medium »
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