Kuldeep Paul

Kuldeep Paul

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

How to Ensure Reliability in RAG Pipelines

How to Implement Effective AI Observability for Reliable Model Monitoring

AI applications are now complex, multi-agent systems that span prompts, retrieval-augmented generation (RAG) pipelines, tool calls, and model routers across multiple providers. Reliability in such systems is not a function of any single model; it is the result of disciplined AI observability: structured visibility into real-time behavior, quality metrics, and

How to Ensure Reliability in RAG Pipelines

How to Ensure Reliability in RAG Pipelines

Retrieval-augmented generation (RAG) has become the default pattern for grounding large language models (LLMs) in domain-specific knowledge. Yet shipping reliable RAG systems requires more than “add a vector database and call it a day.” Reliability emerges from design choices across chunking, retrieval, generation, evaluation, and observability, each with measurable trade-offs

Testing LLM Applications with Maxim AI: A Practical, End-to-End Guide

Testing LLM Applications with Maxim AI: A Practical, End-to-End Guide

Modern AI applications depend on reliable large language models (LLMs), yet without disciplined testing, they risk hallucinations, inconsistent behavior, and costly regressions in production. This guide offers a comprehensive, step-by-step approach to testing LLM applications using Maxim AI’s unified platform for simulation, evaluation, and observability. It synthesizes best practices

How to Evaluate AI Agents Before Production: A Practical, End-to-End Framework

How to Evaluate AI Agents Before Production: A Practical, End-to-End Framework

Pre-production evaluation is the difference between shipping a reliable AI agent and deploying a brittle system that fails under real-world scenarios. Teams that invest in rigorous agent evaluation reduce incident rates, control costs, and accelerate iteration cycles. This guide provides a structured framework (grounded in practical examples and linked to

Human Annotations for Strong AI Evaluation Pipelines

Human Annotations for Strong AI Evaluation Pipelines

Building reliable AI applications requires more than automated testing. While AI evaluation metrics provide speed and scalability, human annotations remain essential for capturing quality signals that automated systems cannot fully measure. This blog explains how human annotations integrate into evaluation pipelines, why they matter for AI quality assurance, and how

Implementing Evals and Observability for LangChain AI Applications with Maxim AI

Implementing Evals and Observability for LangChain AI Applications with Maxim AI

Building reliable AI applications requires robust observability and disciplined evaluation. When your agents and RAG pipelines run on LangChain, Maxim AI provides an end-to-end stack to trace every call, assess quality with machine and human evaluators, and continuously improve performance across cost, latency, and accuracy. This guide shows how to

How to Continuously Improve Your LangGraph Multi-Agent System

How to Continuously Improve Your LangGraph Multi-Agent System

Multi-agent systems are becoming increasingly sophisticated, powering complex workflows across research, customer support, and automation tasks. However, as these systems grow in complexity, understanding their behavior, debugging issues, and optimizing performance becomes significantly more challenging. Without proper observability, teams often struggle to identify bottlenecks, trace errors, and measure improvements across