Which of the following statements are true? 1. ChatGPT is here to stay. 2. ChatGPT will change the way people write and research software related development, architecture and implementation. 3. ChatGPT will infringe on other companies’ and individuals’ IP. 4. Companies will overreact to the infringement (real and potential) and life will become harder for the industry…
AI Agents are being developed to read and respond to emails on our behalf, chat on messaging apps, browse the internet, and even make purchases. This means that, with permission, they can access our financial accounts and personal information. When using such agents, we must be cognizant of the agent’s intent and the permissions we grant it to perform actions. When producing AI agents, we need to monitor for external threats that can sabotage them by injecting malicious prompts.
When large language models (LLMs) first became accessible, most of our interactions with them were bound within a single prompt-response cycle. You asked, they answered. But as developers began embedding AI into real systems (IDE copilot etc.), it became clear that prompts alone couldn’t sustain meaningful workflows. AI needed context, memory, and the ability to act, not just chat. That’s where the Model Context Protocol (MCP) enters the picture (to solve the context and ability needs).
Imagine this: you ask a generative AI tool to “summarize last quarter’s procurement activity for compliance reporting.” Within seconds, it produces a well-structured summary, complete with headings and bullet points. So far, so good. Next, you instruct it to email the report to the compliance officer, attach the raw data for audit purposes, and log the interaction in your internal documentation system. Here’s where the system begins to falter. It doesn’t remember which procurement dataset it used in the first step. It requires you to re-specify the compliance officer’s details, the file format, the logging protocol, and the context all over again.
This article explores three key areas: Generative AI and its patterns, the Retrieval-Augmented Generation (RAG) framework, and AWS’s role in supporting this journey. What is Generative AI? Generative AI is a type of artificial intelligence focused on the ability of computers to use models to create content like images, text, code, and synthetic data. The…
Throughout the history of technology, we’ve witnessed the evolution of software applications—from massive monolithic servers to sleek microservices and miniaturized platforms. History, indeed, has a way of repeating itself, and Generative AI is no exception to this cyclical progression. Today when you consume ChatGPT, Gemini, CoPilot the intelligence comes from the centralized computing: Consuming Large…
Chat GPT has sparked a seismic shift in business and technology, embodying the nature of a double-edged sword. On one hand, it rapidly attracted over 100 million users in its first two months; on the other, it navigated a data breach, emerging with just a few scars. As a substantial number of professionals turn to these tools to boost productivity, organizations and IT leadership are devising innovative strategies to incorporate these technologies into their operations without compromising security. Among these advancements, the emergence of Private GPTs stands out as particularly promising.