The DriftRail knowledge base
Understand the system.
Know what to watch.
Field notes on observing, evaluating, and protecting AI in production. Clear answers, practical checks, and the limits that matter.
The reading room / 02
Start with a question.
How to Evaluate LLM Quality
Build a repeatable LLM evaluation with representative tasks, reviewed labels, explicit failure criteria, and versioned results.
How to Implement AI Guardrails
Design AI guardrails around explicit policies, application enforcement, detector errors, and a tested review or fallback workflow.
How to Monitor LLMs in Production
A practical sequence for instrumenting LLM requests, selecting useful alerts, and checking whether production monitoring captures real failures.
How to Prevent Prompt Injection
Reduce prompt injection risk with untrusted-content boundaries, tool authorization, restricted data access, and adversarial tests.
How to Reduce LLM Costs
Reduce LLM costs by measuring usage, retries, cache behavior, and task quality before changing prompts or model routing.
ISO 27001 Compliance for AI Systems
Prepare AI operational evidence for a ISO/IEC 27001 review without treating monitoring or a generated report as certification.
LLM Cost Optimization
A measurement framework for LLM cost optimization: spend by workflow, cost per successful task, quality constraints, and release comparisons.
LLM Observability vs Traditional Monitoring
Traditional APM tools track latency and errors but miss AI-specific risks. Learn why LLM observability requires hallucination detection, safety classification, and compliance features.
OWASP LLM Top 10: Critical Security Risks
The OWASP 2025 LLM Top 10 categories, with an application-focused checklist for selecting controls and preserving test evidence.
RAG Hallucination Prevention
Reduce unsupported RAG answers by testing retrieval coverage, document freshness, claim support, and abstention behavior.
SOC 2 Compliance for AI Applications
Prepare AI operational evidence for a SOC 2 review without treating monitoring or a generated report as certification.
What Are AI Guardrails?
AI guardrails are safety mechanisms that monitor, filter, or block LLM outputs before they reach users. Learn how guardrails prevent harmful content, PII exposure, and policy violations.
What is Agent Simulation?
Agent simulation is the practice of testing AI agents by simulating multi-turn conversations and scenarios before production deployment. Learn how to test agents safely.
What is an AI Agent?
Learn what AI agents are and how they use LLMs to take autonomous actions. Understand agent architectures and safety considerations.
What is Agentic AI?
Learn what agentic AI means. Understand how AI agents work autonomously, their risks, and why monitoring multi-step workflows matters.
What is an AI Agent Loop?
Learn what AI agent loops are and how autonomous AI systems work. Understand the observe-think-act cycle and monitoring requirements.
What is AI Alignment?
Learn what AI alignment means and why it matters. Understand how to ensure AI systems act according to human values and intentions.
What is AI Bias?
Learn what AI bias means, how it manifests in LLMs, and how to detect and monitor for biased outputs in production.
What is AI Caching?
AI caching reuses previous work to reduce repeated computation. Distinguish response caches from provider-managed prompt-prefix caches.
What is AI Content Moderation?
Learn what AI content moderation means. Understand how to detect and filter harmful, toxic, or inappropriate AI-generated content.
What is AI Explainability?
Learn what AI explainability means. Understand why interpretable AI matters for trust, compliance, and debugging LLM applications.
What is AI Fallback?
Learn what AI fallback is and how to build resilient LLM applications. Understand failover strategies and monitoring requirements.
What is AI Governance?
Learn what AI governance means for organizations. Understand policies, risk management, and compliance frameworks for responsible AI deployment.
What is AI Hallucination?
AI hallucination occurs when large language models generate false or fabricated information that sounds confident and plausible. Learn causes, examples, and how to detect them.