Kuldeep Paul

Kuldeep Paul

Agentic AI | LLM | Product Management | Product Marketing | Data Science | SaaS

Top 5 Model Evaluation Tools to Improve Your LLM-Powered Applications

Top 5 Model Evaluation Tools to Improve Your LLM-Powered Applications

Large Language Models (LLMs) are at the heart of today’s AI powered applications, from chatbots and copilots to knowledge management tools. However, teams need to know: * Are outputs factually correct and safe? * Do they remain reliable across scenarios and updates? * Can we monitor issues in production before they spiral

Why Monitoring AI Models Is the Key to Reliable and Responsible AI in 2025

Why Monitoring AI Models Is the Key to Reliable and Responsible AI in 2025

Introduction In the last decade, the AI industry has undergone a transformation that would have seemed improbable just a few years ago. In 2025, AI models (particularly large language models and agentic systems) are not only powering digital assistants and customer support, but are also helping make decisions in healthcare,

Debugging RAG Pipelines: Identifying Issues in Retrieval-Augmented Generation

Debugging RAG Pipelines: Identifying Issues in Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) has rapidly emerged as a cornerstone of modern AI applications, empowering systems to deliver contextually rich and accurate responses by marrying powerful language models with organizational knowledge bases. Yet, as organizations increasingly deploy RAG-powered solutions for search, customer support, recommendation engines, and more, the complexity of these

LLM Observability: How to Monitor Large Language Models in Production

LLM Observability: How to Monitor Large Language Models in Production

TL;DR: LLM observability is the foundation for debugging and improving large language models in production. It combines tracing, evaluation, and monitoring to capture every prompt, generation, and feedback loop across workflows. This guide explains key concepts like traces, spans, and retrievals, and shows how Maxim AI implements full-stack observability

Agent Tracing for Debugging Multi-Agent AI Systems

Agent Tracing for Debugging Multi-Agent AI Systems

Introduction The rapid evolution of large language models has ushered in a new era of multi-agent systems, where teams of autonomous agents collaborate to solve tasks that exceed the capabilities of single turn workflows and simpler RAG pipelines. These systems have become pivotal in domains ranging from conversational AI and

AI Reliability: How to Build Trustworthy AI Systems

AI Reliability: How to Build Trustworthy AI Systems

Introduction Artificial intelligence is rapidly transforming industries, driving innovation, and redefining how organizations operate. However, as AI systems become more pervasive, the imperative to ensure their reliability and trustworthiness intensifies. Building trustworthy AI is not only a technical challenge, it is a multidimensional endeavor that encompasses ethics, governance, transparency, and

How to Ensure Reliability of AI Applications: Strategies, Metrics, and the Maxim Advantage

How to Ensure Reliability of AI Applications: Strategies, Metrics, and the Maxim Advantage

Summarise this Blog with ChatGPT Artificial intelligence is rapidly reshaping industries, from customer support to healthcare, finance, and beyond. Yet, as organisations integrate AI into mission-critical workflows, reliability and AI quality remains the defining factors that separate successful products from costly failures. In a world where AI agents can autonomously