What is RAG (Retrieval-Augmented Generation)? Explained for Interviews
📰 Medium · AI
Learn about RAG, a technique to improve LLMs by augmenting their generation capabilities with retrieval, and why it matters for AI interviews
Action Steps
- Read about RAG on Medium to understand its basics
- Explore the limitations of LLMs and how RAG addresses them
- Research applications of RAG in real-world scenarios
- Apply RAG to a language model to see its impact on generation capabilities
- Compare the performance of LLMs with and without RAG
Who Needs to Know This
NLP engineers and AI researchers can benefit from understanding RAG to improve their language models, while product managers can use this knowledge to inform their product development strategies
Key Insight
💡 RAG improves LLMs by augmenting their generation capabilities with retrieval, addressing their limited knowledge
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💡 RAG (Retrieval-Augmented Generation) enhances LLMs by retrieving external info to improve generation capabilities #AI #NLP
Key Takeaways
Learn about RAG, a technique to improve LLMs by augmenting their generation capabilities with retrieval, and why it matters for AI interviews
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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