Retrieval Augmented Generation
Fuel Generative AI with Retrieval Capabilities
Stemming from the well known limitations of LLMs in providing factually accurate and contextually relevant information, RAG leverages sophisticated algorithms to resolve complex information retrieval tasks within Generative AI systems.
Improving response reliability, RAG is essential for business applications that depend on specific knowledge and certified datasets.
The essence of RAG
RAG enhances Generative AI applications by using a knowledge repository with certified data to generate responses. This process involves extracting relevant information and combining it with the original prompt. It improves the AI's ability to respond to topics it was not trained on and focuses its generation on specific information.This approach forces LLMs to prefer internal and verified data over other data or hallucinations, preserving context and precision thanks to its two main components.
Retrieval Module
This component is responsible for fetching relevant information from a predefined knowledge base or dataset. It ensures that the generated text is grounded in factual data, thereby enhancing the accuracy and reliability of the responses.
Generation Module
This component uses the retrieved information to generate coherent and contextually appropriate text. It employs advanced language models that are trained on large corpora to produce natural-sounding and contextually relevant output.
How it works
RAG architectures process user queries through a robust, well-defined process that is central to the reliability and success of this technique.
Query Input
The system receives an input query from the user. This could be a question, a request for information, or any other form of textual input.
Retrieval Phase
Using sophisticated search algorithms and indexing techniques, the system searches through the knowledge base to find documents related to the input query.
Document Scoring and Selection
Retrieved documents are scored based on their relevance to the query. The most relevant documents are selected for the next phase.
Text Generation
Starting from the selected documents, the generation module uses the LLM to generate a coherent and contextually appropriate response.
Output Delivery
The generated text is delivered as the final response to the user.
Applications
Customer Support
RAG can provide accurate and contextually relevant responses to customer queries, thereby enhancing the overall customer experience.
Virtual Assistants
Virtual assistants, such as chatbots, can leverage RAG to generate informative and helpful responses, improving their utility and user satisfaction.
Content Creation
RAG can assist in content creation by generating contextually relevant text based on a given prompt, which is useful for tasks like report writing, article generation, and social media content creation.
Educational Tools
Educational platforms can use RAG to provide detailed explanations and answers to students’ questions, aiding in the learning process.
Healtcare
RAG can assist medical professionals by retrieving and generating relevant information from medical literature, thereby supporting decision-making processes.
Primary Considerations
AIWave Approach
AIWave RAG architecture enhances system functionality through a layered approach, extending natural language processing abilities with additional modules. By refining user queries for semantic search, and incorporating Ontologies, Named Entities, and Lemma Dictionaries, the system improves retrieval precision.
This architecture allows for greater control and consistency in response generation, minimizing reliance on large language models to streamline computation and manage costs effectively.
