RAG and LLM, what are they?
Glossary
RAG e LLM, what are they?
Virtual assistants are becoming more and more efficient, and their potential is surprising, especially when applied to the optimization of business processes such as Customer Care and Customer Support Services.
A fundamental turning point for improving the performance of assistants, lies in particular in the new RAG technology, which, in combination with LLM, allows an increasingly wider audience to access this category of services, while at the same time allowing the configuration of extremely precise bots and performing.
But what do these acronyms mean?
LLM – Large Language Model
RAG – Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) is an Artificial Intelligence (AI) solution that aims to overcome the limitations of pre-trained Large Language Models (LLMs), as mentioned above.
It combines the flexibility of large linguistic models with the reliability and updating of an ad hoc defined knowledge base, made up of verified documents. Consulting these sources allows you to keep information up to date and reduce the uncertainty associated with generative models. The ultimate goal is to produce high-quality answers that combine the creativity of the LLM with authoritative, verified and context-specific information sources.
Advantages of the RAG
As you may have guessed, RAG technology brings with it a series of considerable advantages, here are the main ones:
1. Easier to implement
When developing a bot, we start with a basic language model (our LLM we saw earlier). Customizing it for your specific needs can be costly in terms of time and resources. RAG offers a cost-effective alternative for integrating new data, making generative AI more accessible.
2. Always up-to-date knowledge
Keeping language models up to date is critical, but can be difficult. Otherwise, the RAG allows the knowledge base to be updated in a very short time, thus ensuring that the Assistant provides reliable information to users.
3. Gain users’ trust
RAG allows information to be precisely attributed to its original source, verifying its provenance. This increases user trust in artificial intelligence and conversational bots.
4. Greater control in training
RAG gives developers more control over the model’s information sources, allowing it to easily adapt to changing needs and intervene in case of errors. This ensures a more secure implementation of the assistant in various applications.
Now that you have understood the full potential of RAG technology combined with LLMs, you are ready to surprise your customers and users with a precise and punctual Customer Care service; but not only that, you will finally be able to free up precious time for you and your team by delegating much of the assistance to the assistant, on the communication channel you prefer.
We recently implemented RAG technology to Dillo’s LLM models: this update allows users of our AI Assistants to configure their bots even more precisely. Talk to our experts for help setting up your smart assistants using RAG.
See you next time!
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- What’s a LLM
- What’s the RAG
- Advantages of RAG
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