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 / 03
Start with a question.
AI Incident Response
Learn how to handle AI incidents. Understand detection, response, and remediation for LLM failures, hallucinations, and safety issues.
What is AI Load Balancing?
Learn what AI load balancing is and how to distribute LLM traffic across providers. Understand strategies for high availability.
What is AI Memory?
Learn what AI memory is and how LLMs maintain context across conversations. Understand memory types, privacy implications, and monitoring needs.
What is a Model Card?
Learn what model cards are and why they matter for AI transparency. Understand how to document AI models for responsible deployment.
What is AI Orchestration?
Learn what AI orchestration means. Understand how frameworks like LangChain coordinate LLMs, tools, and workflows for complex applications.
What is AI Rate Limiting?
Learn what AI rate limiting is and how to manage LLM API quotas. Understand throttling strategies and cost control.
What is AI Red Teaming?
Learn about AI red teaming and adversarial testing for LLMs. Understand how to find vulnerabilities in AI systems before attackers do.
AI Risk Assessment
Learn how to assess AI risks. Understand frameworks for evaluating LLM applications before deployment and ongoing risk monitoring.
What is AI Routing?
Learn what AI routing is and how to select the right model for each query. Understand cost-quality tradeoffs and routing strategies.
What is AI Safety?
Learn what AI safety means for production applications. Understand risks, mitigation strategies, and best practices for safe AI deployment.
What is AI Watermarking?
Learn what AI watermarking means. Understand how invisible markers in AI-generated content enable detection and attribution.
What is the Attention Mechanism?
Learn what attention mechanisms are and how they enable transformers to process context. Understand self-attention and its role in LLMs.
What is Chain of Thought?
Learn how chain of thought prompting improves LLM reasoning. Step-by-step thinking for better AI problem solving.
What is Constitutional AI?
Learn about Constitutional AI, Anthropic's approach to training helpful, harmless, and honest AI systems using a set of principles.
What is Context Window?
Learn about LLM context windows and token limits. Understand how context length affects AI applications and costs.
What is Distributed Tracing for LLMs?
Distributed tracing for LLMs tracks requests across multi-step AI workflows including chains, agents, and tool calls. Learn how to implement end-to-end observability.
What are Embeddings?
Learn how embeddings convert text to vectors for semantic search and RAG. Understand vector representations in AI applications.
What is Few-Shot Learning?
Learn how few-shot learning teaches LLMs through examples. Improve AI outputs with in-context learning techniques.
What is Fine-Tuning?
Learn how fine-tuning customizes LLMs for specific tasks. Understand when to fine-tune vs use prompting and RAG.
What is Function Calling?
Learn how function calling enables LLMs to use external tools and APIs. Understand structured outputs and tool use patterns.
What is LLM Grounding?
Learn what grounding means for LLMs. Understand how grounding reduces hallucinations by connecting AI outputs to verified sources.
What is LLM Inference?
Learn what inference means in the context of large language models, how it differs from training, and why inference optimization matters for production AI.
What is LLM Jailbreaking?
Learn what jailbreaking means for LLMs. Understand how attackers bypass safety guardrails and how to detect jailbreak attempts.
What is LLM Evaluation?
Learn about LLM evaluation methods and metrics. Understand how to measure AI quality, accuracy, and safety in production applications.