A guide to Retrieval-Augmented
Generation (RAG)

Advanced automation is unlocking a universe of possibilities, setting the stage for an era where continuous innovation is not just beneficial but essential to keep outputs maximally relevant and up to date. Welcome to the world of Retrieval-Augmented Generation (RAG). This cutting-edge method in natural language processing (NLP) and machine learning propels the capabilities of generative models to new heights by seamlessly integrating them with retrieval mechanisms.

B_Robots is a pioneer in mastering the ins & outs of this transformative approach. Let’s talk about RAG

_Extend the capabilities of LLMs with RAG

RAG is a relatively new technique that combines the functionality of retrieval-based models with generative models to create responses by using and incorporating information from existing documentation. This method synergizes various models, orchestrating them to produce outputs that are not just contextually relevant but also highly based on up-to-date information.

From another perspective RAG can be seen as a strategy to augment the efficiency of Large Language Models (LLMs), traditionally powered by extensive datasets and billions of parameters. RAG in fact expands the prowess of LLMs by directly accessing specific knowledge bases, bypassing the need for retraining or fine-tuning – thus elevating their performance and applicability.

_Benefits of RAG

By leveraging external knowledge sources, RAG is capable of a deeper understanding of context, enabling more accurate and informative responses. Some other advantages associated with integrating RAG include:

  • Improved response quality: the combination of retrieval and generation mechanisms results in more coherent and relevant outputs.
  • Robustness and adaptability: RAG’s ability to access external knowledge makes it more robust and adaptable to a wide range of NLP tasks and domains, including question answering, dialogue systems, and content generation.
  • Up-to-date information: giving the model access to the most current, reliable facts, and users to the model’s sources ensures that claims can be checked for accuracy and ultimately trusted.
  • Using Semantic Search: RAG can use Semantic Search to interpret a query’s ‘true meaning’ and intent, going beyond merely matching keywords to produce more results that bear a relationship to the original query.
  • Flexibility: RAG can use any specialized database, giving it unprecedented flexibility without the need for heavy finetuning, while also reducing the time to bring such applications online.
  • Cost-effective: not needing to constantly finetune a large model will cut down on operational costs and time required.

_The (complete) RAG flow

  1. A query is made.
  2. The query is passed on to the RAG embedding model.
  3. The RAG embedding model encodes the query into text embeddings that are compared to a dataset of information.
  4. The RAG retriever identifies the most relevant information with its semantic search abilities and converts it into vector embeddings.
  5. The RAG retriever sends the parsed embeddings to the generator.
  6. The RAG generator accepts the embeddings and combines them with the original query.
  7. The RAG generator transmits the generated output to the language model to produce natural-sounding content presented to the user.

 

_Getting started with RAG

Interested in exploring how to integrate RAG into your applications and workflows? At B-Robots, we’re experienced in leveraging the benefits of RAG for several applications, such as:

Question answering systems

RAG excels in answering complex questions by retrieving and synthesizing information from diverse knowledge bases, but also by using any existing chat histories.

The question and answering systems improve search times for answers within internal documentation or combine different pieces of information retrieved from separate document items into one answer.

In conversational AI applications, RAG enhances the system’s ability to generate contextually appropriate responses by using external knowledge available to the system.

Question and answering can also be used in a classification task by comparing existing documentation and classifying incoming queries.

Innovate your search and FAQ functions & gather valuable data insights with RAG.

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