Have you ever wondered how AI agents understand tabular data, such as that in CSV or Excel files? Or how a file loaded into a platform like ChatGPT can be instantly understood and processed? This insight explores the creation of a custom AI agent capable of achieving these tasks from scratch. Context Jen, an AI
In the evolving world of AI, retrieval-augmented generation (RAG) systems have become standard for handling straightforward queries and generating contextually relevant responses. However, as demand grows for more sophisticated AI applications, there is a need for systems that move beyond simple retrieval tasks. Enter AI agents—autonomous entities capable of executing complex, multi-step processes, maintaining state
The Rise of Agentforce: How AI Agents Are Shaping the Future of Work Salesforce wrapped up its annual Dreamforce conference this September, leaving attendees with more than just memories of John Mulaney’s quips. As the swarms of Waymos ferried participants across a cleaner-than-usual San Francisco, it became clear that AI-powered agents—dubbed Agentforce—are poised to transform
The first notable change in the field of language models is the significant expansion of context window sizes and a reduction in token costs. For instance, Anthropic’s largest model, Claude, has a context window exceeding 200,000 tokens, while recent reports indicate that Gemini’s context window can reach up to 10 million tokens. Under such circumstances,
Guide to RAG (Retrieval-Augmented Generation) Retrieval-Augmented Generation (RAG) has become increasingly popular, and while it’s not yet as common as seeing it on a toaster oven manual, it is expected to grow in use. Despite its rising popularity, comprehensive guides that address all its nuances—such as relevance assessment and hallucination prevention—are still scarce. Drawing from
