Challenges Some of the common problems that stand in the way of ramping up on GenAI knowledge are Here are some of the “remediations” for the above objections. Hopefully the common objections are addressed by now. Let’s move on to the guidance portion now. Avoid learning Learn to operate at a level of abstraction. There is NO need to learn the following topics unless you are applying for a role where you need to build LLMs from scratch. As an analogy, when you go to a motor driving school, the driving instructor does NOT teach you how an Internal Combustion…

Agentic AI is the buzzword of the season. And for all the right reasons. But 2 serious drawbacks of Agentic AI are high costs and slow speed due to the usage of reasoning models for crafting a plan for agent invocation (and tool usage) and multi-step LLM calls for finally arriving at the end goal. Model Distillation is a very well placed offering made GA by the hard working team at Amazon just few days ago. What is Model Distillation, eh? Model distillation involves transferring knowledge from a larger, more capable “teacher” model to a smaller “student” model. This process…

Agentic AI is the buzzword of the season. My team and I’ve been solving customer problems using Agentic AI solutions for a while and here are some observations and predictions. Terminology To make it easier to converse related to Agentic AI solutions, we would need to speak the same language. Terms like ReAct, Self-critique, LLM-as-a-judge will become part of our vocabulary. The previous wave of LLM adoptions already achieved this with terms like RAG, CoT & Reasoning. We might see add-ons to the overall design language. This will be similar to how we don’t use a plain rectangle to represent…

In the last couple of years, Generative AI (Gen AI) has swiftly made its mark in software engineering, infiltrating the daily workflows of developers, testers, and managers alike. But before we dive into today’s AI-fueled reality, let’s rewind a few years. Back then, everything was mostly manual, and, things were running just fine! Developers were coding, testers were testing, and project managers? Well, they were juggling all the chaos like seasoned pros. So, why Gen AI now? Were developers suddenly unable to write code? Did testers forget how to break software (in the best way possible)? Did managers lose track…