Kuldeep Paul

Kuldeep Paul

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

Top 5 Tools to Monitor AI Agents in 2025

Top 5 Tools to Monitor AI Agents in 2025

TL;DR: The rapid evolution of AI agents (from simple chatbots to complex, multi-agent systems) has transformed how organizations automate workflows and deliver intelligent services. However, as AI agents become more autonomous and embedded in critical business processes, robust monitoring and observability are essential to ensure reliability, compliance, and continuous

The State of AI Hallucinations in 2025: Challenges, Solutions, and the Maxim AI Advantage

The State of AI Hallucinations in 2025: Challenges, Solutions, and the Maxim AI Advantage

Introduction Artificial Intelligence (AI) has rapidly evolved over the past few years, with Large Language Models (LLMs) and AI agents now powering mission-critical applications across industries. Yet, as adoption accelerates, one persistent challenge continues to undermine trust and reliability: AI hallucinations. In 2025, hallucinations (instances where AI generates factually incorrect

Agent Evaluation vs Model Evaluation: What’s the Difference and Why It Matters

Agent Evaluation vs Model Evaluation: What’s the Difference and Why It Matters

Introduction As artificial intelligence systems become more complex and increasingly agentic, the distinction between model evaluation and agent evaluation has become both critical and nuanced. While the evaluation of underlying models (such as large language models, LLMs) remains foundational, the rise of AI agents (autonomous entities capable of multi-step reasoning,

What Is Prompt Engineering? A Comprehensive Guide for Modern AI Teams

What Is Prompt Engineering? A Comprehensive Guide for Modern AI Teams

Introduction Prompt engineering has rapidly emerged as a critical discipline in the development and deployment of AI systems, particularly large language models (LLMs) and agentic workflows. As organizations strive to build reliable, context-aware, and high-performing AI solutions, the importance of crafting, refining, and managing prompts cannot be overstated. This blog

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

TL;DR Retrieval-Augmented Generation (RAG) combines retrieval and generation to produce accurate, context-rich responses, but debugging these systems can be complex. Common issues include irrelevant retrievals, hallucinations, context overflow, and latency. Effective debugging requires evaluating retrieval quality (precision, recall, MRR, MAP) and generation accuracy (faithfulness, relevance, correctness) while tracing and