Reranking and fusion techniques improve retrieval quality by reordering or combining results from different retrieval methods.
Reranking takes initial search results and reorders them using more sophisticated (and often more computationally expensive) models to improve relevance. Fusion combines results from multiple retrieval methods (like keyword search and semantic search) to leverage the strengths of each approach.
These techniques significantly improve retrieval quality by addressing the limitations of individual retrieval methods. They help balance precision and recall, combining the strengths of different approaches to deliver more relevant results to the LLM.
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Machine learning is a subset of artificial intelligence...
NLP is a field of AI that gives computers the ability to understand text...
Deep learning is a subset of machine learning that uses neural networks...
Reinforcement learning is training algorithms to make sequences of decisions...
Computer vision enables machines to interpret and make decisions based on visual data...
Building a robust Reranking / Fusion solution is challenging. Respeak's Enterprise RAG Platform handles this complexity for you.