Kamya Shah

Kamya Shah

RAG Evaluation: A Complete Guide for 2025

RAG Evaluation: A Complete Guide for 2025

TL;DR • RAG systems combine retrieval and generation; evaluation must assess both components. • Retrieval quality hinges on recall, precision, relevance, and timeliness of sources. • Generation quality requires grounding, faithfulness, clarity, and low hallucination rates. • Judge reliability improves with mixed methods: LLM-as-a-judge, programmatic checks, and human review. • Use

Maximizing AI Agent Observability: How to Set Up the Custom Dashboard on Maxim for End-to-End Monitoring

Maximizing AI Agent Observability: How to Set Up the Custom Dashboard on Maxim for End-to-End Monitoring

TL;DR Setting up the dashboard on Maxim for AI agent observability enables engineering and product teams to monitor, debug, and optimize agentic workflows in real time. By leveraging distributed tracing, custom dashboards, and advanced evaluation features, Maxim AI empowers teams to ensure reliability, transparency, and rapid iteration for their

Understanding Agentic RAG, Choosing the Right tool for RAG Observability and Why Maxim Leads

Understanding Agentic RAG, Choosing the Right tool for RAG Observability and Why Maxim Leads

TL;DR Agentic RAG adds autonomous reasoning to retrieval-augmented generation, and it demands rigorous observability: Maxim AI leads with unified simulations, RAG evaluation, and observability for AI reliability in production. Agentic RAG blends retrieval-augmented generation with ai agents that plan, call tools, and adapt based on outcomes. The

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

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,

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. Introduction Retrieval-Augmented Generation (RAG) systems