Guides

Introduction As AI agents transition from experimental prototypes to production-critical systems, teams need comprehensive platforms that support the entire AI lifecycle. While evaluation is essential, building reliable agents requires simulation capabilities, detailed tracing, and seamless collaboration between engineering and product teams. Braintrust has established itself as a platform for evaluation

Table of Contents * TL;DR * Understanding AI Hallucinations * Why Hallucination Detection Matters * Key Criteria for Evaluating Detection Tools * Top 5 Hallucination Detection Tools * 1. Maxim AI * 2. Langfuse * 3. Arize AI * 4. Galileo * 5. LangSmith * Comparative Analysis * Detection Methodology Framework * Best Practices for Implementation * Conclusion TL;DR AI hallucinations pose

The artificial intelligence landscape experienced an unprecedented release cycle in late 2025, with three frontier models launching within weeks of each other. Google's Gemini 3 Pro arrived on November 18, followed by Claude Opus 4.5 from Anthropic on November 24, both building upon OpenAI's GPT-5

Debugging software is traditionally a deterministic process. In standard engineering, if Function A receives Input X, it should invariably produce Output Y. When it doesn't, you inspect the stack trace, identifying the exact line of code where logic broke down. Debugging Large Language Models (LLMs) and AI Agents

Retrieval-Augmented Generation (RAG) has become the dominant architectural pattern for connecting large language models with external knowledge sources. The RAG market is projected to grow from $1.85 billion in 2025 to over $67 billion by 2034, reflecting a compound annual growth rate of 49%. This explosive growth has created

TL;DR Managing AI agent performance in production requires a systematic approach across measurement, monitoring, and optimization. The ten critical factors include establishing clear task success metrics, optimizing latency and response times, controlling costs, implementing robust error handling, building comprehensive observability infrastructure, designing effective evaluation frameworks, ensuring data quality, integrating