Kamya Shah

Kamya Shah

LLM Hallucinations in Production: Monitoring Strategies That Actually Work

LLM Hallucinations in Production: Monitoring Strategies That Actually Work

TL;DR: LLM hallucinations occur when AI models generate factually incorrect or unsupported content with high confidence. In production, these failures erode user trust and cause operational issues. This guide covers the types of hallucinations, why they happen, and proven monitoring techniques including LLM-as-a-judge evaluation, semantic similarity scoring, and production

How to Reduce LLM Cost and Latency: A Practical Guide for Production AI

How to Reduce LLM Cost and Latency: A Practical Guide for Production AI

TL;DR Running large language models in production can quickly become expensive and slow without proper optimization. Organizations often face monthly bills exceeding $250,000 and response times that frustrate users. This guide explores proven strategies to reduce LLM costs by 30-50% and latency by up to 10x through intelligent

Load Balancing in AI Gateway: A Comprehensive Guide

Load Balancing in AI Gateway: A Comprehensive Guide

TL;DR Load balancing in AI gateways distributes incoming LLM requests across multiple providers, models, or API keys to ensure high availability, optimal performance, and cost efficiency. This guide covers core load balancing strategies, how Bifrost implements intelligent load balancing with automatic failover, and best practices for production AI applications.

Beginner's Guide to Tracking Token Usage

Beginner's Guide to Tracking Token Usage

TL;DR Token tracking is essential for controlling costs, optimizing performance, and maintaining transparency in AI applications. Without visibility into token consumption, organizations face unpredictable bills, inefficient resource allocation, and difficulty attributing costs across teams. This guide covers the fundamentals of token tracking, common challenges in multi-provider environments, and practical

Top 5 LLM Routing Techniques

Top 5 LLM Routing Techniques

TL;DR LLM routing is the process of intelligently directing queries to the most appropriate model based on factors like complexity, cost, latency, and domain expertise. This guide covers the top 5 routing techniques that production teams use to optimize their AI infrastructure: 1. Semantic Routing - Uses embedding-based similarity

Top 5 Tools to Evaluate RAG Performance in 2026

Top 5 Tools to Evaluate RAG Performance in 2026

TL;DR Evaluating RAG (Retrieval-Augmented Generation) systems is critical for ensuring accurate, reliable AI responses. This guide covers five leading RAG evaluation platforms: Maxim AI, LangSmith, Arize Phoenix, RAGAS, and DeepEval. While all platforms offer RAG evaluation capabilities, they differ significantly in scope, ease of use, and production readiness. Maxim

5 AI Observability Platforms for Multi-Agent Debugging

5 AI Observability Platforms for Multi-Agent Debugging

TL;DR Multi-agent systems present unique debugging challenges that traditional monitoring tools cannot address. This guide examines five leading AI observability platforms built for multi-agent debugging: Maxim AI (end-to-end simulation, evaluation, and observability platform), Arize (enterprise ML observability with OTEL-based tracing), Langfuse (open-source LLM engineering platform), Braintrust (evaluation-first platform with