Over the past few days, I’ve been exploring OpenClaw and trying to run parts of it locally. As someone working on production GenAI systems, I’m always curious about how different frameworks approach building AI agents that operate in real environments. What stood out while studying OpenClaw is something I keep seeing across many GenAI systems: … Continue reading What OpenClaw’s Architecture Taught Me About Building Real GenAI Systems(part 1)
Tag: #aiforproduction
Will Jobs Exist as We Know Them? (The Big Picture)
In Part 1(https://aiforproduction.blog/2026/02/22/will-jobs-exist-as-we-knowthe-fear/) , we saw something uncomfortable. An AI agent was able to independently build a model — potentially better than a human-built solution — and even generate a GDPR compliance report. At first glance, the conclusion seems obvious: AI automates coding → developers disappear. But that assumption misses something important. Building the model … Continue reading Will Jobs Exist as We Know Them? (The Big Picture)
Will Jobs Exist as we know?(The Fear)
This series started because of a 1–1 conversation with a reportee. One of my reportees asked me a simple question: “With everything happening in AI… should I be worried, and what will happen to jobs in 5-10 years?” It wasn’t a dramatic question. It wasn’t about headlines. It was about career, growth, and uncertainty. That … Continue reading Will Jobs Exist as we know?(The Fear)
Optimizing RAG Systems: A Deep Dive into Chunking Strategies.
When it comes to today’s AI systems, especially in Generative AI, the main challenge isn’t just building a basic system with, say, 70% accuracy—it’s about pushing that system to over 90% and making it reliable for real-world production. Optimizing RAG (Retrieval-Augmented Generation) systems is essential for reaching this level. Effective chunking, one of the foundational … Continue reading Optimizing RAG Systems: A Deep Dive into Chunking Strategies.
GPU vs CPU: Understanding Their Differences and Benefits for AI Processing
A lot of the buzz is around GEN AI from the last couple of years. The focus on GPU has increased significantly. Building a good understanding of why we need each and when is essentially to build our foundational understanding. So let's dive in. We will start by looking at the theory of both, then … Continue reading GPU vs CPU: Understanding Their Differences and Benefits for AI Processing
RAG Components – 10,000 ft Level
When you look at GEN AI and specifically LLM's from a usage point of view, we have a few techniques to interact with LLM's. This depends on whether you need to interact with external data or just use LLMs for your different tasks. RAG is an important technique and widely adopted because it helps you … Continue reading RAG Components – 10,000 ft Level
Understanding RAG and Vector DB
For anyone in the field of AI and ML, they probably do know all of these terms. But how do you explain these to someone new. Let's try understanding this. USE CASE: Mark wants to plan a trip to Georgia in Europe and he wants to use an LLM for this. INTERACTION WITH LLM: Attempt … Continue reading Understanding RAG and Vector DB
AI For Production 