However, once you choose a foundational model, you’ll still need to customize it to your business, so your model can deliver results that address your challenges and needs. RAG can be a great fit for your LLM application if you don’t have the time or money to invest in fine-tuning. ...
As mentioned earlier, RAG-enabled chatbots are currently the dominant form of LLM applications in production. However, the scope of LLM applications has widened to cover code execution within your infrastructure. This expansion is due to foundation models created by companies like OpenAI, Anthropic,...
With these in place, we can now use Langflow to create a RAG-enabled pipeline.Sign into Langflowand choose the "Vector Store RAG" template: Data preparation The foundation of any RAG system is good data. Before we can start to ask our LLM about our documents, we need to load our docu...
Generative AIhas the potential to transform every industry. Human workers are already usinglarge language models(LLMs) to explain, reason about, and solve difficult cognitive tasks.Retrieval-augmented generation(RAG) connects LLMs to data, expanding the usefulness of LLMs by giving them access to...
LLM fine-tuning vs retrieval-augmented generation (RAG) vs retrieval-augmented fine-tuning (RAFT) (source: arxiv) In their paper, the researchers compare RAG methods to “an open-book exam without studying” and fine-tuning to a “closed-book exam” where the model has memorized information ...
In this post, we’ll walk through how to use LlamaIndex and LangChain to implement the storage and retrieval of this contextual data for an LLM to use. We’ll solve a context-specific problem with RAG by using LlamaIndex, and then we’ll deploy our solution easily to Heroku. Before we...
According to Google Cloud, RAG (Retrieval-Augmented Generation) is an AI framework combining the strengths of traditional information retrieval systems (such as databases) with the capabilities of generative large language models (LLMs). By combining this extra knowledge with its ...
Finally, if the LLM is using external data through a Retrieval-Augmented Generation or RAG system, how do we judge whether it makes correct use of these data? In a two-part series, we will explore evaluation protocols for generative AI. This post focuses on text generation and Large ...
Retrieval Augmented Generation (RAG) seems to be quite popular these days. Along the wave of Large Language Models (LLM’s), it is one of the popular techniques to get LLM’s to perform better on…
Both RAG and fine-tuning make generic AI models more useful in a particular field or for a specific use case. RAG gives an LLM access to a company’s internal data stores; that helps the LLM provide more targeted responses, which is critical for use cases that rely on up-to-d...