Information Evolution is proud to introduce an important advancement from conventional data acquisition to an AI/ML-based mechanism.
Agentic RAG and RAG/GAI are a paradigm shift in the way we find data. Agentic RAG uses artificial intelligence agents to independently plan and carry out retrieval operations, drawing information from pre-defined external sources. This iterative approach provides accurate, relevant data, and minimizes the chance of errors. In contrast to static retrieval, Agentic RAG can adapt to evolving data sources and needs, automatically responding to changes in website structure and content.
RAG with Generative AI (GAI) combines retrieval with generative AI, which can help us create correct summaries, reports, and insights from the information retrieved. By using retrieval as a guide, RAG/GAI generates data that is consistent, accurate, and aligned with pre-specified boundary conditions.
In addition, our Internet researchers use advanced prompting techniques. Prompt engineering—the process of writing, refining and optimizing inputs to encourage generative AI systems to create specific, high-quality outputs—is a crucial skill for them, since good prompt engineering optimizes the quality and relevance of AI models’ results.
We’re also upgrading our infrastructure to manage the greater computational requirements of AI and machine learning tasks. Upgrades include new servers, GPU upgrades, flexible storage solutions, and expanded network infrastructure. The goal is a strong, flexible system to support growing AI/ML programs and plans.
Besides improving data quality and automating report generation, Agentic RAG’s flexibility enables us to scale data collection easily, keeping up with increasing volumes and multiple sources. This anticipates our clients’ need for advanced, dependable, and effective data acquisition. In embracing AI/ML methods, we show our commitment to innovation and excellence.
