RAG's Evolution: From Simple Retrieval to Agentic AI
Search engines evolved from simple keyword matching to sophisticated semantic and hybrid systems, improving their ability to understand user intent through language models and embeddings, but challenges remain in achieving true comprehension.
MAIN POINTS FROM TRANSCRIPT
- Early search systems relied on keyword indexing and ranking methods like TF-IDF and BM25.
- Semantic search introduced vector representations to understand word meanings and user intent.
- Hybrid systems combined keyword precision with semantic recall for improved search results.
- Large language models predict likely responses based on learned patterns but face comprehension challenges.
TAKEAWAYS
- Search engines initially struggled with understanding language and user intent.
- Semantic search uses vectors to map words by meaning, enhancing search accuracy.
- Hybrid systems leverage both keyword and semantic approaches for better results.
- Large language models revolutionized search but still face limitations in true understanding.