Kuldeep Paul

Kuldeep Paul

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

Advanced Prompt Engineering Techniques in 2025

Advanced Prompt Engineering Techniques in 2025

Prompt engineering has evolved from a trial-and-error practice into a systematic discipline backed by rigorous research. As organizations deploy increasingly complex AI applications (from conversational agents to multi-agent systems) the gap between experimental prompting and production-grade prompt management has become critical. This comprehensive guide examines state-of-the-art prompt engineering techniques, explores

The Definitive Guide to Prompt Engineering 2025

The Definitive Guide to Prompt Engineering 2025

Prompt engineering has emerged as a critical discipline for developing and optimizing interactions with large language models (LLMs). As organizations increasingly deploy AI applications in production, the ability to craft effective prompts directly impacts application quality, reliability, and user experience. What is Prompt Engineering? Prompt engineering is the process of

Monitor, Troubleshoot, and Improve AI Agents with Maxim AI

Monitor, Troubleshoot, and Improve AI Agents with Maxim AI

AI agents are fundamentally different from traditional software systems. They make decisions autonomously, interact with external tools, process unstructured data, and generate outputs that vary even with identical inputs. This non-deterministic behavior creates unique monitoring and debugging challenges for engineering teams deploying production AI systems. Traditional application monitoring approaches, tracking

6 Ways Simulation Based Testing Accelerates AI Agent Reliability

6 Ways Simulation Based Testing Accelerates AI Agent Reliability

AI agents deployed in production face unpredictable user interactions, edge cases, and failure modes that are difficult to anticipate during development. Traditional testing approaches (unit tests, integration tests, and manual QA) prove insufficient for systems that generate non-deterministic outputs, handle unstructured inputs, and make autonomous decisions. When quality issues emerge

Prompt Versioning: Best Practices for AI Engineering Teams

Prompt Versioning: Best Practices for AI Engineering Teams

Prompt engineering drives AI agent quality, but without systematic version control, teams face cascading production issues. A single untracked prompt change can degrade output quality across thousands of user interactions, introduce safety violations, or break downstream integrations, often without immediate detection. As AI applications grow in complexity, prompt versioning transforms

Prompt Chaining for AI Engineers: A Practical Guide to Improving LLM Output Quality

Prompt Chaining for AI Engineers: A Practical Guide to Improving LLM Output Quality

Large language models face significant challenges when handling complex, multi-faceted tasks within a single prompt. Prompt chaining (a systematic approach that decomposes complex operations into sequential, focused subtasks) offers engineering teams a scalable pattern for improving reasoning quality, output reliability, and observability. This guide defines prompt chaining, examines the research

LLM Hallucination Detection and Mitigation: Best Techniques

LLM Hallucination Detection and Mitigation: Best Techniques

Large language models can generate fluent, convincing responses, even when they're factually wrong. For engineering teams deploying AI agents in production, robust hallucination detection and mitigation isn't optional. It's the foundation of trustworthy AI, product reliability, and user safety. This guide explains the mechanisms