The first step to enabling a RAG-based LLM solution is to build a knowledge base. This private collection of data can include a variety of text-based sources, such as the company handbook and product briefs. You will need to do some work to prepare your data for efficient processing, incl...
LLM Response Generation:The LLM takes into account both the original query and the retrieved contexts to generate a comprehensive and relevant response. It synthesizes the information from the contexts to ensure that the response is not only based on its pre-existing knowledge but is also augmented...
What are some challenges associated with RAG? While RAG is a powerful tool for enhancing an LLM’s capabilities, it is not without its limitations. Like LLMs, RAG is only as good as the data it can access. Here are some of its specific challenges: ...
RAG isn’t the only technique used to improve the accuracy of LLM-based generative AI. Another technique is semantic search, which helps the AI system narrow down the meaning of a query by seeking deep understanding of the specific words and phrases in the prompt. ...
Easier to train.Because RAG uses retrieved knowledge sources, the need to train the LLM on a massive amount of data is reduced. Can be used for multiple tasks.Aside from chatbots, RAG can be fine-tuned for various specific use cases, such as text summarization and dialogue systems. ...
–RAG is a system that retrieves facts from an external knowledge base to provide grounding for large language models (LLMs). This grounding ensures that the information generated by the LLMs is based on accurate and current data, which is particularly important given that LLMs can sometimes ...
Retrieval Augmented Generation (RAG) is a technique to enhance the results of a generative AI or Large Language Model (LLM) solution. Perhaps the best way to understand RAG is to first look at how generative AI traditionally works, and why that poses a risk to companies seeking to leverage ...
LLMs. The large language model is what's used to process the initial input to figure out what context is required to respond accurately. It's also used to generate a response to the finalized prompts. Some complex RAG-based apps have multiple LLMs in their pipelines: small language models...
Like a good judge, large language models (LLMs) can respond to a wide variety of human queries. But to deliver authoritative answers that cite sources, the model needs an assistant to do some research. The court clerk of AI is a process calledretrieval-augmented generation, or RAG for shor...
Combine the power of the latest LLMs with your unique enterprise knowledge. Botpress is a flexible and endlessly extendable AI chatbot platform. It allows users to build any type of AI agent or chatbot for any use case – and it offers the most advanced RAG system in the market. ...