Kamya Shah

Kamya Shah

Top 7 Challenges in Building RAG Systems and How Maxim AI is the best Solution

Top 7 Challenges in Building RAG Systems and How Maxim AI is the best Solution

TL;DR RAG systems fail when retrieval is weak, prompts drift, context is misaligned, or evaluation is missing. Maxim AI addresses these failure modes with agent simulation, offline and online evals, prompt management, and production observability across traces, spans, tool calls, and datasets. Teams ship reliable RAG faster by continuously

Hallucination Evaluation Frameworks: Technical Comparison for Production AI Systems (2025)

Hallucination Evaluation Frameworks: Technical Comparison for Production AI Systems (2025)

TL;DR Hallucination evaluation frameworks help teams quantify and reduce false outputs in LLMs. In 2025, production-grade setups combine offline suites, simulation testing, and continuous observability with multi-level tracing. Maxim AI offers end-to-end coverage across prompt experimentation, agent simulation, unified evaluations (LLM-as-a-judge, statistical, programmatic), and distributed tracing with auto-eval pipelines.

How to Stress Test AI Agents Before Shipping to Production

How to Stress Test AI Agents Before Shipping to Production

TL;DR AI agents are failing in production at alarming rates, with over 40% of projects expected to be canceled by 2027 due to inadequate testing and unclear business value. Recent benchmarks show frontier models failing basic tasks up to 98% of the time. This article explores why traditional testing

Designing Evaluation Stacks for Hallucination Detection and Model Trustworthiness

Designing Evaluation Stacks for Hallucination Detection and Model Trustworthiness

TL;DR Building trustworthy AI systems requires comprehensive evaluation frameworks that detect hallucinations and ensure model reliability across the entire lifecycle. A robust evaluation stack combines offline and online assessments, automated and human-in-the-loop methods, and multi-layered detection techniques spanning statistical, AI-based, and programmatic evaluators. Organizations deploying large language models need

How context drift impacts conversational coherence in AI systems

How context drift impacts conversational coherence in AI systems

TL;DR Context drift degrades conversational coherence in AI systems by causing models to lose track of established information across multi-turn interactions. This phenomenon leads to responses misaligned with user intent, particularly during extended sessions where the AI gradually shifts away from the original topic. Technical factors including limited context

Diagnosing and Measuring AI Agent Failures: A Complete Guide

Diagnosing and Measuring AI Agent Failures: A Complete Guide

TL;DR AI agents present unique diagnostic challenges due to their non-deterministic behavior and autonomous decision-making capabilities. Microsoft's AI Red Team catalogued failures in agentic systems through internal red teaming and systematic interviews with external practitioners, identifying security failures that result in loss of confidentiality, availability, or integrity,

Guardrails in Agent Workflows: Prompt-Injection Defenses, Tool-Permissioning, and Safe Fallbacks

Guardrails in Agent Workflows: Prompt-Injection Defenses, Tool-Permissioning, and Safe Fallbacks

TL;DR Agent workflows require robust security mechanisms to ensure reliable operations. This article examines three critical guardrail categories: prompt-injection defenses that protect against malicious input manipulation, tool-permissioning systems that control agent actions, and safe fallback mechanisms that maintain service continuity. Organizations implementing these guardrails with comprehensive evaluation and observability