LLM Evaluation & Security

A deep dive into what Harness Engineering is and how to build an Agent Harness. Explore the 'Agent = Model + Harness' formula and learn how to build reliable AI infrastructure.

Master the practical strategies of Harness Engineering. Learn how to extend Agent capabilities with the MCP protocol, build complex self-healing workflows using LangGraph, and design reliable Human-in-the-Loop (HITL) mechanisms.

Explore the core principles of Large Language Model Jailbreak attacks, such as DAN attacks, role-playing bypasses, and encoding deception. This article provides cutting-edge Semantic Guardrails strategies to help you build secure AI applications.

Design a reproducible Agent Harness for AI systems. Learn how to isolate tools, replay scenarios, inject failures, enforce step and cost budgets, evaluate task outcomes and safety, and compare judge-assisted scoring without exposing private chain-of-thought.

Learn when ROUGE, BLEU, and exact match are useful, why they miss open-ended quality, and how to use an LLM judge without treating it as ground truth. This guide covers task-specific rubrics, deterministic oracles, pairwise randomization, human calibration, RAG evidence checks, privacy, cost, and release gates.

A deep dive into LLM Guardrails principles and engineering. Covers NeMo Guardrails, Guardrails AI, and Llama Guard. Includes Python/Node.js examples for building safe, reliable, and hallucination-free AI applications.

A rigorous guide to evaluating language models when public scores are incomplete evidence. Learn how contamination, saturation, annotator disagreement, Goodhart effects, judge bias, and workload mismatch distort benchmarks, then build versioned task sets, deterministic oracles, calibrated human or model review, cost and latency measurements, and release gates.

Explore the escalating battle between AI web crawlers and content publishers. From traditional robots.txt to Cloudflare's AI Labyrinth and legal challenges, learn how the web is defending itself against unauthorized AI training data collection.

Design AI Agent memory around purpose limitation, data minimization, provenance, retention, access control, deletion propagation, and evidence. Explains why vector deletion, summaries, caches, backups, fine-tuning, and model outputs need separate treatment under GDPR-style privacy programs.

A practical engineering guide to EU AI Act compliance before the August 2026 deadline—covering risk classification, audit logging, bias testing, and conformity assessment implementation.