Guides

Context Window Management: Strategies for Long-Context AI Agents and Chatbots

Context Window Management: Strategies for Long-Context AI Agents and Chatbots

Context window management has emerged as a critical challenge for AI engineers building production chatbots and agents. As conversations extend across multiple turns and agents process larger documents, the limitations of context windows directly impact application performance, cost, and user experience. Modern language models offer context windows ranging from 8,

From Black Box to Glass Box: Achieving Transparency with AI Observability

From Black Box to Glass Box: Achieving Transparency with AI Observability

Modern AI systems (LLM-powered chatbots, RAG pipelines, and autonomous agents) often feel opaque. Teams wrestle with questions like “Why did the agent make that decision?” or “Which prompt or retrieval step caused the failure?” Moving from a black box to a glass box demands systematic instrumentation across the AI stack,

The Technical Guide to Managing LLM Costs: Strategies for Optimization and ROI

The Technical Guide to Managing LLM Costs: Strategies for Optimization and ROI

LLM costs have become a critical concern for engineering teams deploying AI applications at scale. In 2025, API pricing ranges from $0.25 to $15 per million input tokens and $1.25 to $75 per million output tokens, creating significant budget variability depending on model selection and usage patterns. Organizations

Solving the 'Lost in the Middle' Problem: Advanced RAG Techniques for Long-Context LLMs

Solving the 'Lost in the Middle' Problem: Advanced RAG Techniques for Long-Context LLMs

TLDR: Long-context LLMs often miss information placed mid-sequence (“lost in the middle”), driven by positional biases like RoPE decay. Production fixes: two-stage retrieval (broad recall + cross-encoder reranking), hybrid search (semantic + BM25), and strategic ordering (top evidence at start and end). Strengthen chunking with contextual retrieval; keep only the most relevant

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

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,