/

Modernizing Search with Generative AI

Modernizing Search with Generative AI

Harnessing the Power of Retrieval-Augmented Generation (RAG) through Pureinsights Discovery

Retrieval-Augmented Generation (RAG) is fast becoming a benchmark practice in the world of search and generative AI. This blog explores why you should implement RAG and how platforms like Pureinsights Discovery streamline the process and leverages it to best advantage.

Understanding RAG

Retrieval Augmented Generation is a technique that enhances the accuracy and relevance of generative responses by using vector-based information retrieval. In this approach, a search query is first converted into a vector representation, which is then used to perform a similarity search within a database of document vectors. The most relevant document vectors, representing the closest matches to the query are retrieved, along with its associated text chunk, and used as context. This context is then fed into a Large Language Model (LLM) to generate a detailed and coherent answer. The generative nature of the model means it creates responses dynamically rather than retrieving pre-written answers from a database.

Why a RAG-enabled search platform like Discovery is essential for effective Generative AI integration.

Here are some of the reasons:

Grounded Responses: RAG improves the accuracy of responses by grounding the generative process in specific, retrieved data. This reduces the likelihood of the LLM generating incorrect or fabricated information, commonly known as hallucinations, because the responses are based on actual, relevant data retrieved from trusted sources.

Controlled Data Access: RAG systems can be designed to respect and enforce organizational access controls. The retrieval component can be configured to access only authorized content that the search user can see, ensuring that sensitive or confidential information is handled appropriately and not exposed beyond authorized users.

Contextual Retrieval: RAG leverages search engines to retrieve the most relevant documents or data chunks for a given query. This ensures that the LLM receives highly relevant context, which improves the precision and relevance of the generated responses.

How Pureinsights Discovery implements RAG

Data Ingestion:  The first step in creating an effective RAG system is data ingestion. This involves collecting data from diverse sources, ensuring it is clean, and structuring it for further processing. Data cleaning involves removing duplicates, correcting errors, and filtering out irrelevant content to maintain high data quality, while data formatting standardizes the data into a consistent format suitable for indexing and retrieval.

Indexing: Once the data is ingested, it needs to be indexed to facilitate quick and relevant retrieval. This includes breaking down documents into smaller chunks and generating embeddings for these chunks. These chunk embeddings are stored in a search engine index, allowing for efficient vector search and retrieval.

Query Processing: To provide relevant answers, the system must first understand the user’s query. Query understanding involves parsing and interpreting the query to extract its intent and key components, which are then converted into an embedding that captures its semantic meaning.

Retrieval Phase: Discovery uses the query embedding to find relevant document chunks from the index through a vector search. By comparing the query embedding with document chunk embeddings, Discovery pinpoints the best matches.

Contextual Augmentation: Discovery selects the most relevant document chunks and augments them with metadata to create a coherent context. This enriched context is merged with the user query to form a prompt with clear instructions and relevant details, guiding the LLM to generate precise and well-informed responses.

Answer Generation: The structured prompt is given to a large language model (LLM), which uses its pre-trained knowledge and the provided context to generate a detailed answer. This answer then undergoes post-processing to ensure clarity and correctness.

Response Delivery: Finally, Discovery delivers a generative answer to the user, potentially including links to original documents for further reading.

RAG Architecture

Discovery as a Testbed for Rapid Prototyping in Generative AI Solutions

One of Discovery’s standout features is its capability to serve as a testbed for rapid prototyping. This allows developers to experiment with various Large Language Models (LLMs), chunking strategies, data models and business rules to refine and enhance the quality of generative responses. By automating and optimizing each step of the Retrieval-Augmented Generation (RAG) process, Discovery simplifies the implementation of effective RAG-based search, copilot, and chat solutions, making it easier for organizations to achieve superior results.

If you want to find out more about how you could transform your search experience with Pureinsights Discovery, CONTACT US and we’ll provide a personalized consultation to show you the possibilities.

Additional Resources

LinkedIn
Twitter

Stay up to date with our latest insights!