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Retrieval Augmented Generation (RAG) systems are revolutionizingAIby enhancing pre-trained language models (LLMs) with external knowledge.
Leveraging vectordatabases, organizations are crafting RAG systems tailored to internaldatasources, amplifying LLM capabilities.
This fusion is reshaping how AI interprets user queries, delivering contextually relevant responses across domains.
Senior Director of Products and Solutions at Pliops.
RAG systems extend the capabilities of LLMs by integrating enterprise data sources dynamically with information during the inference phase.
How RAG can help
The benefits of RAG can be classified into the following categories.
This is crucial for tasks that require accuracy and up-to-date knowledge.
This means the answers you get are more likely to be on point and useful.
Second, selecting relevant data sources are fundamental steps in building a successful RAG system.
Using SpaCY or NLTK libraries provides context-aware chunking via named entity recognition and dependency parsing.
Deploying these models in production environments can be challenging due to their high resource requirements.
Storing large amounts of data can incur significant costs, especially when using cloud-based storage solutions.
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