AI Observability
Tracing, evals, drift detection, cost telemetry, hallucination monitoring — keeping production AI systems honest.
11 articlesin AI Observability
Token Tracking and Cost Management
Monitor token usage and costs — per-request tracking, budgets, cost allocation across teams, and the optimization strategies that shrink the bill.
Phoenix: Open-Source LLM Observability
Arize's open-source, OTel-native LLM observability stack — traces, eval workflows, and prompt experiments, self-hosted with no data leaving your VPC.
Validating LLM Outputs
Validate LLM outputs — schema conformance, factuality checks against source documents, toxicity detection, and structured output parsing before serving users.
OpenTelemetry for LLM Applications
Instrument LLM apps with OpenTelemetry — the gen-ai semantic conventions, distributed tracing across RAG pipelines, and vendor-neutral export to any backend.
Online Evals: Production Quality Monitoring
Production-time evaluation — implicit user feedback, A/B testing, and real-time LLM-as-judge scoring for continuous quality monitoring after ship.
LLM Monitoring in Production
Production dashboards for LLM apps — latency, error rates, token usage, and quality drift detection, wired into the alerting you already trust.
LLM Latency: Measurement and Optimization
Measure and optimize LLM response times — Time to First Token, tokens per second, P99 tail latency, and the streaming/caching levers that actually move the needle.
LangSmith: LLM Observability Platform
LangChain's observability platform — distributed tracing, dataset curation, evaluation suites, and the prompt playground, for LangChain and framework-agnostic apps alike.
LLM Input Validation Strategies
Validate and sanitize LLM inputs — prompt injection detection, PII filtering, and content moderation that run before the model call to prevent misuse and cut cost.
Content Safety for AI Applications
Detect and filter harmful, biased, or inappropriate AI content — moderation APIs, classifiers, and the multi-layer approach from input filtering to human review.
LLM Evaluation Beyond Vibes
Systematic approaches to evaluating LLM outputs — automated metrics, human evaluation frameworks, regression testing, and building evaluation pipelines.