how-rag-architecture-overcomes-llm-limitations 
Data Science Dojo
FEBRUARY 8, 2024
Since NLP techniques operate on textual data, which inherently cannot be directly integrated into machine learning models designed to process numerical inputs, a fundamental question arose: how can we convert text into a format compatible with these models? How are enterprises using embeddings in their LLM processes?
AWS Machine Learning Blog
DECEMBER 5, 2023
New and powerful large language models (LLMs) are changing businesses rapidly, improving efficiency and effectiveness for a variety of enterprise use cases. Speed is of the essence, and adoption of LLM technologies can make or break a business’s competitive advantage. SGT’s applicability is not limited to language models.
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AWS Machine Learning Blog
NOVEMBER 7, 2023
Large language models (LLMs) with their broad knowledge, can generate human-like text on almost any topic. However, their training on massive datasets also limits their usefulness for specialized tasks. Furthermore, the cost to train new LLMs can prove prohibitive for many enterprise settings.
Mlearning.ai
JANUARY 1, 2024
Perhaps, the most critical step in the entire RAG value chain is searching and retrieving the relevant pieces of information (known as documents). MMR considers the relevance of each document only in terms of how much new information it brings given the previous results.
AWS Machine Learning Blog
DECEMBER 6, 2023
To create AI assistants that are capable of having discussions grounded in specialized enterprise knowledge, we need to connect these powerful but generic LLMs to internal knowledge bases of documents. However, the popular RAG design pattern with semantic search can’t answer all types of questions that are possible on documents.
AWS Machine Learning Blog
MAY 14, 2024
You can learn how to improve your LLMs with RLHF on Amazon SageMaker, see Improving your LLMs with RLHF on Amazon SageMaker. This can also be a ruled-based method that can determine where, when and how your expert teams can be part of generative AI – user conversations.
Towards AI
OCTOBER 16, 2023
Photo by Markus Spiske on Unsplash This is the second part of the RAG analysis: part 1: Disadvantages of RAG part 2: Practical Considerations in RAG Application Design The RAG (Retrieval Augmented Generation) architecture has been proven to be efficient in overcoming the LLM input length limit and the knowledge cutoff problem.
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