Kamya Shah

Kamya Shah

Guide to Managing Human Annotation in AI Evaluation: Best Practices

Guide to Managing Human Annotation in AI Evaluation: Best Practices

Human annotation remains the gold standard for training and evaluating AI systems, yet managing annotators effectively presents significant challenges for AI teams. As enterprises scale their AI applications, establishing robust annotation workflows becomes critical to maintaining model quality and reliability. This guide explores evidence-based practices for managing human annotation in

How to Ensure Consistency in Multi-Turn AI Conversations

How to Ensure Consistency in Multi-Turn AI Conversations

Multi-turn conversations represent one of the most challenging aspects of building reliable AI agents. While large language models demonstrate impressive capabilities in isolated interactions, maintaining consistency across extended dialogues remains a critical challenge for AI engineers. Research shows that leading LLMs exhibit significantly lower performance in multi-turn conversations than single-turn

A Step-by-Step Guide to Building Robust Evaluation Datasets for AI Agents

A Step-by-Step Guide to Building Robust Evaluation Datasets for AI Agents

TL;DR Building robust evaluation datasets is critical for measuring and improving AI agent performance. This guide covers the complete lifecycle of dataset creation—from defining objectives and sourcing data to curating multi-modal examples and implementing continuous improvement workflows. Quality evaluation datasets enable teams to run meaningful evaluations, catch regressions

A Comprehensive Guide to Testing and Evaluating AI Agents in Production

A Comprehensive Guide to Testing and Evaluating AI Agents in Production

TL;DR Testing and evaluating AI agents in production requires a systematic approach that combines pre-deployment validation, continuous monitoring, and iterative improvement. This guide covers essential strategies including simulation-based testing, multi-layered evaluation frameworks, real-time observability practices, and data curation techniques. Organizations can achieve reliable AI agent deployments by implementing comprehensive

How to Successfully Manage Prompt Versions for Scalable AI Deployments

How to Successfully Manage Prompt Versions for Scalable AI Deployments

TL;DR Managing prompt versions effectively is critical for scaling AI applications reliably. Without systematic versioning, organizations face unpredictable outputs, difficult rollbacks, and deployment failures that contribute to the 95% of AI pilot programs that fail to deliver measurable impact. This guide explores proven strategies for prompt versioning, including semantic

How to Implement Effective A/B Testing for AI Agent Prompts

How to Implement Effective A/B Testing for AI Agent Prompts

TL;DR A/B testing is essential for optimizing AI agent prompts and improving application performance. This systematic approach allows teams to compare prompt variations, measure real-world impact, and make data-driven decisions. Research shows that prompt design significantly affects output quality, with variations producing performance differences of up to 40%

A Comprehensive Guide to Preventing AI Agent Drift Over Time

A Comprehensive Guide to Preventing AI Agent Drift Over Time

TL;DR AI agent drift degrades system performance over time through model updates, data distribution changes, and prompt variations. Research shows 91% of ML systems experience performance degradation without proactive intervention. Prevention requires continuous monitoring, automated evaluation pipelines, prompt version control, and comprehensive observability. Teams using platforms like Maxim AI