Kuldeep Paul

Kuldeep Paul

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

Version Control for Prompts: The Foundation of Reliable AI Workflows

Version Control for Prompts: The Foundation of Reliable AI Workflows

TL;DR: Prompt version control is indispensable for building robust, scalable, and trustworthy AI systems. As generative AI applications mature, the ability to systematically manage, track, and deploy prompt changes is as critical as code versioning in traditional software engineering. This blog explores the principles and best practices of prompt

How to Perform A/B Testing with Prompts: A Comprehensive Guide for AI Teams

How to Perform A/B Testing with Prompts: A Comprehensive Guide for AI Teams

TL;DR: A/B testing with prompts is a foundational strategy for optimizing AI agent performance, reliability, and user experience. By systematically comparing different prompt versions, teams can identify the most effective configurations for their LLMs and agents in real-world scenarios. This guide explores the principles, best practices, and tooling—

Observability for AI Agents: LangGraph, OpenAI Agents, and Crew AI

Observability for AI Agents: LangGraph, OpenAI Agents, and Crew AI

TL;DR: This blog provides a comprehensive guide to observability for AI agents—specifically focusing on LangGraph, OpenAI Agents, and Crew AI. It covers why observability is essential for reliable, scalable agentic systems, explores the unique architectures and debugging strategies of each framework, and demonstrates how platforms like Maxim AI

Detecting Hallucinations in LLM Powered Applications with Evaluations

Detecting Hallucinations in LLM Powered Applications with Evaluations

TL;DR: Hallucinations in large language model (LLM) powered applications undermine reliability, user trust, and business outcomes. This blog explores the nature of hallucinations, why they occur, and how systematic evaluations—both automated and human-in-the-loop—are critical for detection and mitigation. Leveraging platforms like Maxim AI enables teams to build

AI Hallucinations in 2025: Causes, Impact, and Solutions for Trustworthy AI

AI Hallucinations in 2025: Causes, Impact, and Solutions for Trustworthy AI

TL;DR AI hallucinations—plausible but false outputs from language models—remain a critical challenge in 2025. This blog explores why hallucinations persist, their impact on reliability, and how organizations can mitigate them using robust evaluation, observability, and prompt management practices. Drawing on recent research and industry best practices, we

Top 3 Tools in 2025 to Experiment with Prompts

Top 3 Tools in 2025 to Experiment with Prompts

TL;DR Prompt experimentation is the backbone of building robust, reliable, and high-performing AI systems in 2025. This blog explores the top three tools that are shaping the landscape of prompt engineering, featuring Maxim AI alongside other industry-leading platforms. Each tool offers unique capabilities for prompt management, evaluation, and deployment,

How to Evaluate AI Agents: Comprehensive Strategies for Reliable, High-Quality Agentic Systems

How to Evaluate AI Agents: Comprehensive Strategies for Reliable, High-Quality Agentic Systems

TL;DR Evaluating AI agents requires a rigorous, multi-dimensional approach that goes far beyond simple output checks. This blog explores the best practices, metrics, and frameworks for AI agent evaluation, drawing on industry standards and Maxim AI’s advanced solutions. We cover automated and human-in-the-loop evaluations, workflow tracing, scenario-based testing,