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How RAG Changes the Way AI Understands and Generates Answers

1 July 2024
Anat Bielsky

In recent years, with the rapid advancement in artificial intelligence, particularly in Natural Language Processing (NLP), Large Language Models (LLMs) have been developed with impressive capabilities for text generation, translation, and question-answering. However, these models are often limited to the static information on which they were trained. To overcome this limitation, the Retrieval-Augmented Generation (RAG) method was developed, which combines language models with specific information sources, to improve the quality and relevance of generated information in a defined and known content world.

RAG provides models with sources they can cite, like footnotes in a research article, so users can verify any explanation given by the model.

Using RAG has several significant advantages:

  1. Improved accuracy - Using specific information sources, such as an internal organizational database, allows the model to generate more accurate and up-to-date answers.

  2. Enables users to verify the source of information.

  3. Time and cost savings - RAG can avoid the need to re-train models with each information update, saving time and expensive computing resources.

  4. Expanding the knowledge base - Integration of specific information sources allows models access to a broader knowledge base than what could be provided based on training alone.

RAG has a wide range of potential applications in various industries:

  • Customer service – This can significantly improve the quality of customer responses, by providing personalized and accurate answers.

  • Content creation - Writers and content creators can use RAG to quickly incorporate up-to-date and relevant information into the materials they create.

  • Medicine - Can be used to provide accurate and current medical information in response to patient or doctor questions.

  • Knowledge management - Organizations can implement RAG in their organizational knowledge management systems, to ensure access to complete and reliable information for all employees.

Despite the great promise of RAG, implementing these systems has significant challenges and limitations:

  • Dependence on the quality of retrieved information - The success of RAG applications largely depends on the quality of the database on which content retrieval is based. Outdated, inaccurate, or incomplete information can lead to incorrect and problematic answers.

  • Challenges in integrating information from different sources - Seamlessly and consistently integrating information from various heterogeneous sources is a significant challenge. Gaps, contradictions, or mismatches between information sources can impair the coherence and accuracy of generated answers.

  • Limitations of language models - The performance of language models themselves, despite significant progress, is still not perfect. Models may sometimes generate incorrect, overly general, or insufficiently contextualized answers, especially for complex queries.

  • Need for output control - To ensure the quality and accuracy of answers, human involvement is often required in system definition, selection of information sources, and output control. Full automation of the process is still challenging.

  • Computational resource costs - Despite savings compared to re-training models, RAG systems still require significant computing and storage resources, especially given the need to index and process large amounts of text from a wide variety of sources.

Therefore, future developments of RAG are expected to address these challenges and limitations and focus on several directions, such as:

  • Improving retrieval algorithms to ensure higher accuracy and relevance of retrieved information.

  • Developing advanced techniques for merging information from diverse sources.

  • Improving the language models themselves so they can better consider context and generate more accurate and richer answers.

  • Building tools that allow developers and organizations to design and customize RAG systems more easily.

It can be said that the Retrieval-Augmented Generation represents an important step in the development of Artificial Intelligence models for language processing. Its ability to incorporate external information enables more accurate and up-to-date responses and saves expensive computing costs. With continued research and development of the technology, RAG systems are expected to significantly improve human-computer interaction and provide great value in a wide range of application areas. At the same time, it's important to be aware of the technology's limitations and challenges and to strive for its continuous improvement.

A bit about RAG's operating principles

RAG systems operate in two main stages - the retrieval stage and the generation stage.

Retrieval stage:

  1. The system receives a query from the user.

  2. Based on this query, it searches for a specific information source, such as the organization's internal knowledge base.

  3. From the information found, the system selects and retrieves the text or documents most relevant to the query.

Generation stage:

  1. The system combines the information retrieved in the previous stage with the user's original query.

  2. This combination creates the context or basis on which the language model will operate.

  3. The language model (LLM) receives the created context, and based on it, it formulates and generates the best and most appropriate answer to the user's original question.

Thus, ultimately, the user receives an answer created specifically for their query, utilizing the most up-to-date and relevant information retrieved from the specific knowledge source.

In conclusion, it can be said that despite the current challenges, RAG represents a significant step forward in AI capabilities for language processing and the response capability provided to users.

A robot hand holding a question mark
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