What is RAG (Retrieval-Augmented Generation)? Explained for Interviews
📰 Medium · RAG
Learn about RAG, a technique that enhances Large Language Models by augmenting their generation capabilities with retrieval, and why it matters for improving their performance
Action Steps
- Read about RAG on Medium to understand its basics
- Explore how RAG can be applied to improve language model performance
- Configure a RAG model using a library like Hugging Face Transformers
- Test the RAG model on a specific task, such as question answering
- Compare the results of the RAG model with a traditional language model
Who Needs to Know This
NLP engineers, AI researchers, and data scientists can benefit from understanding RAG to improve the capabilities of their language models and address their limitations
Key Insight
💡 RAG can improve language model performance by allowing them to retrieve information beyond their training data
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🤖 Learn about RAG, a technique that enhances Large Language Models with retrieval! 📚
Key Takeaways
Learn about RAG, a technique that enhances Large Language Models by augmenting their generation capabilities with retrieval, and why it matters for improving their performance
Full Article
One of the biggest limitations of Large Language Models (LLMs) is that they only know what they learned during training. Continue reading on Medium »
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