Kuldeep Paul

Kuldeep Paul

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

Understanding RAG Pipelines: Architecture, Challenges, and Best Practices

Understanding RAG Pipelines: Architecture, Challenges, and Best Practices

Retrieval-Augmented Generation has emerged as a foundational architecture for enterprise AI applications. According to recent surveys, over 60% of organizations are developing AI-powered retrieval tools to improve reliability and reduce hallucinations in their AI systems. For AI engineers and product managers building context-aware applications, understanding RAG pipelines is essential for

A Complete Guide to Tracing and Evaluating RAG Pipelines

A Complete Guide to Tracing and Evaluating RAG Pipelines

Retrieval-Augmented Generation (RAG) is a powerful technique that enhances large language models (LLMs) by connecting them to external knowledge sources. First introduced in a 2020 research paper by Meta AI, this approach allows AI applications to provide more accurate, current, and contextually relevant responses. While RAG pipelines offer significant advantages,

LLM-as-a-Judge vs Human-in-the-Loop Evaluations: A Complete Guide for AI Engineers

LLM-as-a-Judge vs Human-in-the-Loop Evaluations: A Complete Guide for AI Engineers

Modern LLM-powered systems don’t behave like traditional software. The same input can yield different outputs depending on sampling parameters, context, upstream tools, or even seemingly harmless prompt changes. Models are updated frequently, third‑party APIs change under the hood, and user behavior evolves over time. All of this makes

A Comprehensive Guide to Ensuring Reliable Performance in AI Agents

A Comprehensive Guide to Ensuring Reliable Performance in AI Agents

As AI agents transition from experimental prototypes to mission-critical enterprise applications, ensuring their reliability has become a strategic imperative. Recent benchmark testing shows that systematic evaluation frameworks can achieve 95% error detection and 86% error localization accuracy, demonstrating that reliable AI agents are not just aspirational, they're achievable

Best Practices for Prompt Management in AI Applications

Best Practices for Prompt Management in AI Applications

In the rapidly advancing field of artificial intelligence, the quality of an AI application's output is fundamentally tied to the quality of its inputs. These inputs, known as prompts, are the instructions that guide large language models (LLMs) to produce desired results. As organizations move AI applications from

How to Accelerate Your AI Agent Development Cycle: Tools and Strategies

How to Accelerate Your AI Agent Development Cycle: Tools and Strategies

The AI agent development landscape has reached a critical inflection point. According to Gartner's 2025 research, over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls. Yet simultaneously, industry forecasts predict that at

The Importance of Observability: Why Your AI Agents Need It

The Importance of Observability: Why Your AI Agents Need It

The artificial intelligence industry is experiencing a troubling paradox. While AI adoption has reached unprecedented levels, with enterprises investing $30 billion to $40 billion in generative AI pilots in 2024, the failure rate has simultaneously skyrocketed. Recent analysis from S&P Global Market Intelligence reveals that 42% of companies