Kamya Shah

Kamya Shah

5 Best Tools for Prompt Versioning

5 Best Tools for Prompt Versioning

TL;DR Prompt versioning helps teams manage, version and track changes of their prompts across environments. This blog explains how to implement prompt versioning in practice and compares five tools- Maxim, PromptLayer, Helicone, LangSmith, and Portkey, against criteria like version control, labels, eval integrations, analytics, collaboration, and production governance. Introduction:

LLM Monitoring : A complete guide for 2025

LLM Monitoring : A complete guide for 2025

TLDR LLM monitoring is the discipline of observing AI systems end-to-end covering prompts, parameters, tool calls, retrievals, outputs, cost, and latency to overcome the limits of traditional Application Programming Monitoring (APM) in non-deterministic, multi-step agent workflows. The guide outlines core risks (hallucination, prompt injection, response variance, and cost-performance tradeoffs) and

How to Evaluate Your AI Agents Effectively?

How to Evaluate Your AI Agents Effectively?

Evaluating AI agents is essential for reliability. Real-world interactions expose non-determinism, model updates, and hallucinations, which degrade trust without rigorous checks. A structured evaluation approach (pre-release and in production) helps quantify quality, prevent regressions, and align systems to human preference. Maxim AI provides unified capabilities across offline testing, online evaluations,

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 Maxim’s offline

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 challenge is

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