#observability
15 articles
Tracing and observability tooling: LangSmith, Langfuse, Braintrust, and Arize compared
A production-honest comparison of LangSmith, Langfuse, Braintrust, and Arize Phoenix for LLM tracing, evaluation, and quality monitoring in 2026.
Prompt Versioning, Evaluation, and LLMOps: Treating Prompts Like Code
How production teams version, test, and regression-test prompts the same way they ship software — CI/CD gates, eval datasets, and the tools that make it tractable.
The data flywheel: closing the LLM feedback loop in production
How to run a data flywheel in production: capture implicit and explicit user signals, curate them into fine-tuning data, and compound model quality every cycle.
The LLM Gateway Pattern: Rate Limiting, Cost Attribution, and Observability
How a centralized AI gateway gives you virtual keys, dual TPM/RPM rate limiting, per-request cost tagging, and provider fallback — and when to build vs buy.
Agent Evaluation: Trajectories, Tool Calls, and Multi-Step Correctness
End-to-end task success is not enough for agents. Learn to evaluate at three levels: outcome, trajectory soundness, and per-component correctness — with code.
Agent Evaluation and Why Agents Fail in Production
Why 85% of agent pilots never reach production scale — compounding error rates, LLM-judge bias, and the six evaluation layers that catch what offline tests miss.
A/B Testing Prompts and Models in Production
How to run statistically valid experiments on LLM prompt variants and model swaps — shadow mode, canary rollout, LLM-as-judge scoring, and when to call the winner.
LLM Observability: Monitoring Quality, Cost, and Drift in Production
Traditional APM tells you the HTTP call succeeded. LLM observability tells you whether the answer was any good — covering traces, quality scores, cost anomalies, and drift.
Golden Datasets: Building the Ground Truth Your Evals Depend On
A concrete recipe for constructing and maintaining LLM evaluation datasets — sourcing, sizing, versioning, and wiring them into CI so regressions are caught before they ship.
LLM-as-Judge: Calibrating Your Automated Evaluator
How to build an LLM judge that actually agrees with your domain experts — covering bias failure modes, rubric design, calibration targets, and production cost math.
Offline vs Online Evaluation: Building Your Two-Layer Safety Net
Pre-deployment regression suites catch what you break; production sampling catches what happens to you. Here is how to build both layers and wire them together.
Reference architecture for a production LLM application
The seven-layer blueprint for production LLM apps: ingress, AI gateway, context assembly, model execution, output validation, observability, and feedback store.
Why Eval Is Hard: The Measurement Problem in LLM Applications
Standard software metrics miss quality regressions in LLM apps. Learn why subjectivity, Goodhart traps, and silent failures demand a dedicated eval discipline.
Eval First: Building the Safety Net Before You Need It
Why LLM eval is hard, how 50 real examples beat 5,000 synthetic ones, and how to wire offline CI gates and production sampling into a two-layer safety net.
LLMOps: Shipping and Operating AI Products
The operational layer that separates lasting AI products from perpetual pilots: gateways, eval pipelines, prompt CI/CD, observability, and the data flywheel.