Kamya Shah

Kamya Shah

Effective Strategies for RAG Retrieval and Improving Agent Performance

Effective Strategies for RAG Retrieval and Improving Agent Performance

TL;DR Retrieval-Augmented Generation (RAG) systems and AI agents face performance challenges that directly impact accuracy, latency, and user satisfaction. In 2025, organizations achieve 35-48% improvements in retrieval precision and up to 80% success rates in task completion by implementing advanced strategies including adaptive retrieval patterns, multimodal content integration, hybrid

Prompt Management and Collaboration for AI Agents Using Observability and Evaluation Tools

How to Streamline Prompt Management and Collaboration for AI Agents Using Observability and Evaluation Tools

TL;DR Managing prompts for AI agents requires structured workflows that enable version control, systematic evaluation, and cross-functional collaboration. Observability tools track agent behavior in production, while evaluation frameworks measure quality improvements across iterations. By implementing prompt management systems with Maxim’s automated evaluations, distributed tracing, and data curation capabilities,

Top Practical AI Agent Debugging Tips for Developers and Product Teams

Top Practical AI Agent Debugging Tips for Developers and Product Teams

TL;DR: Debugging AI agents requires a systematic approach that combines observability, structured tracing, and evaluation frameworks. This guide covers practical techniques including distributed tracing for multi-agent systems, root cause analysis using span-level debugging, leveraging evaluation metrics to identify failure patterns, and implementing real-time monitoring with automated alerts. Teams using

Intuitive UI for Prompt Management: Ship AI Faster Without Code Changes

Intuitive UI for Prompt Management: Ship AI Faster Without Code Changes

TL;DR An intuitive prompt management UI anchored to versioning, deployment variables, and integrated evaluators enables safe, measurable, and reversible changes to AI applications in production. With Maxim AI, teams can organize and version prompts, compare quality, cost, and latency across models, run agent simulations, and apply automated observability with

Prompt Experimentation with Maxim's Prompt Playground

Prompt Experimentation with Maxim's Prompt Playground

TL;DR Prompt experimentation is critical to building reliable, production-grade AI applications. Maxim's Prompt Playground provides teams with a comprehensive platform for iterating, testing, and deploying prompts at scale. Key features include side-by-side prompt comparison, multimodal support, prompt versioning, prompt tools, and seamless deployment workflows. This article explores

Explore how AI prompt experimentation can unlock effective, scalable prompt management

Explore how AI prompt experimentation can unlock effective, scalable prompt management

TL;DR Prompt experimentation turns prompt designing into an engineering discipline: Define hypotheses, execute controlled experiments across models and parameters, assess performance using quantitative and human‑in‑the‑loop metrics, and version and deploy the best‑performing configuration.. With Maxim AI, teams centralize prompt management, automate evals in pre-release and

The Modern AI Observability Stack: Understanding AI Agent Tracing

The Modern AI Observability Stack: Understanding AI Agent Tracing

TL;DR AI agent tracing has become critical for building reliable AI applications at scale. Modern observability stacks enable teams to track agent behavior across multi-step workflows, debug failures in production, and systematically improve quality. This guide explores how distributed tracing adapts to AI systems, covering trace hierarchies, span-level monitoring,