Technical writing from the Future AGI team
Evaluation patterns, observability techniques, and lessons from building AI systems in production.
How to Evaluate LLM Responses Without Human Raters
Automated evaluation methods that let small teams run continuous quality checks at scale, without relying on expensive annotation rounds.
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Tracing AI Agent Chains: What Observability Tools Get Wrong
Most observability tools were built for microservices, not agent chains. Here is what breaks when you apply them to LLM pipelines without adjustment.
Hallucination Rate vs. Accuracy: The Metrics Teams Actually Use
A breakdown of the evaluation metrics that matter in production AI, and why hallucination rate and answer accuracy measure different failure modes.
Building an Evaluation Harness for RAG Systems
Step-by-step guide to setting up automated evaluation for retrieval-augmented generation pipelines, covering retriever quality and answer faithfulness.
When Your AI Agent Drifts: Early Warning Signs
Model behavior shifts without you changing a line of code. Learn the leading indicators that catch drift before users report it.
Scoring LLM Outputs: Rubrics, Model Judges, and Human-in-the-Loop
Three approaches to scoring LLM outputs, when each one fits, and how to combine them for production-grade evaluation coverage.
Integrating Observability Into Your LangChain Pipeline
A practical walkthrough for adding trace logging and evaluation hooks to an existing LangChain agent without restructuring your code.
Production AI Failures We Have Seen: A Technical Post-Mortem
Four failure patterns from real AI deployments, how they went undetected for weeks, and the evaluation gaps that let them slip through.
The Hidden Costs of AI Hallucinations
Hallucinations create measurable costs in support volume, correction work, and lost deals. Learn how to estimate their effect on your product.
Benchmark Your LLM: A Practical Engineering Guide
Build internal benchmarks around real use cases rather than leaderboard tasks, then run them continuously as your model changes.
How AI Agent Testing Differs From Software Testing
Unit tests catch deterministic code regressions. LLM variability calls for another testing model. Here is how to adjust the approach.
Why Future AGI Began With Evaluation
Future AGI began with a way to measure AI quality, not a dashboard or API. Here is why evaluation came first.