Kamya Shah

Kamya Shah

Prompt versioning and its best practices 2025

Prompt versioning and its best practices 2025

TL;DR Prompt versioning is essential for building reliable, scalable, and trustworthy AI applications. By adopting best practices such as prompt versioning, clear documentation of prompt versions, robust rollback strategies, and leveraging tools like Maxim AI, teams can ensure prompt quality, streamline collaboration, and minimize operational risks which include unintended

Top 5 Tools to Ensure RAG Observability

Top 5 Tools to Ensure RAG Observability

What is RAG Observability and why is it important? RAG observability is the practice of monitoring and debugging retrieval-augmented generation systems across the entire flow: query intake, retrievals, LLM generations, and final responses. It matters because RAG pipelines combine external context with model outputs, making failures harder to diagnose than

7 Important metrics to track for AI Agent Observability

7 Metrics You Should Track for AI Agent Observability

TL;DR This articles talks about the seven core metrics you should track for AI Agent Observability: Step Completion, Step Utility, Task Success, Tool Selection, Toxicity, Faithfulness, and Context Relevance at session, trace, and span levels. Instrument distributed tracing, attach automated evals with human review where needed, and correlate quality

Scneraio-based Testing: Maxim's Test Suite for Reliable, Production-Ready AI Agents

Scenario-Based Testing: Maxim’s Test Suite for Reliable, Production-Ready AI Agents

TL;DR Scenario-based testing makes AI agents reliable by validating behavior across realistic, multi-turn conversations, diverse personas, tools, and context sources. Traditional testing falls short because agents are non-deterministic, and multi-turn conversational. Maxim AI provides an end-to-end platform: Prompt IDE, agent simulations, unified evaluators, and enterprise observability to scale evaluations,

Best framework for RAG Observability

Best framework for RAG Observability

TL;DR Maxim AI offers the most comprehensive framework for RAG observability, combining end-to-end tracing, evaluation, and monitoring for AI agents. Its full-stack platform enables technical teams to debug, monitor, and optimize retrieval-augmented generation (RAG) systems with reliability, and speed. Sign up for Maxim AI to experience best-in-class RAG observability.

AI Agent Quality Assurance with Maxim: A Comprehensive Guide

AI Agent Quality Assurance with Maxim: A Comprehensive Guide

TL;DR Maxim AI delivers a full-stack platform for AI agent quality assurance, combining simulation, evaluation, and observability to help engineering and product teams ship reliable, high-performing agentic applications. This guide explains how Maxim’s tools enable robust agent monitoring, debugging, and continuous improvement, with actionable insights and best practices

7 Key features of Agent Observability

7 Key features of Agent Observability

TL;DR Agent observability is essential for building reliable, high-quality AI applications. This blog explores seven foundational features that empower engineering and product teams to monitor, debug, and optimize AI agents at scale. From distributed tracing to automated evaluations, discover how Maxim AI’s observability suite delivers actionable insights and