Find What You Need, Faster: The Power of Generative AI in Search
In the world of technology, search capabilities often go unnoticed. However, they play a crucial role in shaping applications and contributing to the development of generative AI. As we entered 2022, it became clear that search technology was about to undergo a significant transformation, especially with the growing popularity of cloud-based applications.
The Birth of SWIRL Metasearch: A New Approach to Information Retrieval
How SWIRL Emerged
In late 2022, a notable shift occurred in the digital landscape. More applications moved to the cloud, becoming accessible through standard protocols like HTTP and authentication methods such as OAUTH2. This standardization created an ideal environment to revisit the concept of federated search.
Overcoming Ranking Challenges
One of the persistent issues in search technology has been the re-ranking of results. Before the advent of Large Language Models (LLMs), this was a complex problem due to insufficient training data. However, in 2023, SWIRL made a breakthrough by integrating LLMs, significantly improving the processing and presentation of diverse search results.
The Evolution of AI: From Data Duplication to Smart Retrieval
The Limitations of Centralized AI Systems
Throughout 2023, many vendors promoted an AI approach that involved copying data into centralized systems and using fine-tuning and Retrieval-Augmented Generation (RAG) to generate insights. However, this method quickly revealed its limitations, particularly in the form of AI-generated hallucinations – incorrect or fabricated information produced by AI models.
The Impracticality of Mass Data Transfer
As organizations began to realize the impracticality and potential risks of copying vast amounts of data into AI systems, a critical question emerged: How can businesses effectively utilize AI without creating new data silos?
Enhancing RAG with Advanced Search Capabilities
The Power of Leaving Data in Place
The solution lies in leveraging existing search technologies instead of moving data. By allowing generative AI to search for information on behalf of the user, we can avoid many of the inefficiencies associated with traditional AI implementations.
Reducing Hallucinations Through Smart Retrieval
When combined with skilled prompt engineering, RAG models can significantly reduce hallucinations. These models enhance the AI’s ability to deliver accurate and relevant information, boosting productivity without requiring massive data transfers. With search running in the background, retrieving the right content for the generative AI, RAG can provide substantial benefits.
The Financial and Logistical Case for Smart Search in AI
The Cost of Data Duplication
A report by Goldman Sachs highlighted the enormous financial and logistical burdens of duplicating data into AI systems. For large corporations, attempting to consolidate thousands of data silos into a single AI framework could be prohibitively expensive and complex.
SWIRL: A Unified Search Interface for AI
SWIRL addresses these challenges by providing a unified search interface for generative AI. This approach allows users to access and utilize information without data duplication, streamlining interactions and ensuring comprehensive insights across various data points and platforms.
Real-World Impact: SWIRL in Action
Transforming Supply Chain Management
A practical example of SWIRL’s capabilities can be seen in its collaboration with a large manufacturer. Their supply chain group, which previously spent significant time navigating between SAP, Collibra, MS Teams, and Snowflake, found a SWIRL solution requiring no data movement. This enabled them to ask complex natural language questions and receive answers from across their critical applications, including direct responses from built-in AIs, datasets, charts/graphs, document snippets, and Teams messages.
The Future of AI and Search
As generative AI evolves in enterprise settings, it will increasingly serve as a productivity-enhancing assistant. While AI alone cannot fully resolve the complexities of messy, incomplete, and sometimes contradictory data within enterprises, search-enabled AI can significantly aid in navigating this information labyrinth without exacerbating existing challenges.
Unleashing the Potential of Search-Powered AI
Integrating search with generative AI offers a promising path forward, avoiding the inefficiencies of past approaches and aligning with the practical realities of enterprise environments. This combination exemplifies how, when thoughtfully applied, technology can transform challenges into opportunities for innovation and efficiency. The power of search is the secret ingredient in the evolving landscape of generative AI.