As GEN AI evolves, multi-agent systems (MASs) are gaining traction, yet many remain at the PoC stage. So this is where research papers and surveys help. Over the weekend, I explored their failure points and found a fascinating study worth sharing. MASs promise enhanced collaboration and problem-solving, but ensuring consistent performance gains over single-agent frameworks … Continue reading Why Do Multi-Agent LLM Systems Fail? Insights from Recent Research
Category: System Design & Production
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.
Evaluation and Experiment Tracking for LLM’s
Have you ever done text generation with an LLM and though: I wish I could track experiments and evaluate different LLM results in terms of cost, sample comparison and memory together like below? If yes this article for you. Experimentation tracking is one critical piece of model development which is very important as it helps … Continue reading Evaluation and Experiment Tracking for LLM’s
Understanding AI/ML Systems In Production
AI and ML have evolved from complex mysteries few years back to everyday tools, driven by models like ChatGPT and Bard. ChatGPT and Bard made AI easier, but most are still basic because turning them profitable needs rare know-how mostly in big companies. It's not just about Language Models; AI's larger development faces this. Many … Continue reading Understanding AI/ML Systems In Production
AI For Production 