William Latitude

Table of contents * LLM Evaluation: Frameworks, Methods, and Tools for Measuring Quality * What is LLM Evaluation? * Why LLM Evaluation Matters * Non-deterministic outputs require continuous measurement * Production behavior differs from development * Quality degrades silently * Compliance demands documentation * Core LLM Evaluation Methods * 1. LLM-as-Judge * 2. Programmatic Rules * 3. Human-in-the-Loop * 4. Composite Evaluation

Table of contents * What is prompt optimization? * Manual iteration vs automatic prompt engineering * How automatic prompt optimization works * Evaluation-driven optimization * Types of evaluations for prompt optimization * What to look for in prompt optimization tools * When prompt optimization makes sense * Use automatic optimization when: * Use manual iteration when: * Common tradeoffs in

Table of contents * AI Reliability & Trustworthiness: Principles, Frameworks, and How to Assess Them * What is AI reliability? * Why AI reliability matters in production * What makes an AI system trustworthy? * How to determine if an AI system is trustworthy * 1. Establish baseline measurements * 2. Implement continuous evaluation * 3. Monitor for

Table of contents * What is LLM observability? * Why LLM observability matters * Non-deterministic outputs * Complex, chained pipelines * Hallucinations and accuracy issues * Cost and performance unpredictability * LLM observability vs LLM monitoring * Core components of LLM observability * Traces * Instrumentation * Metrics * How teams implement LLM observability * Instrument your application * Visualize traces * Close the feedback