evals
7 articles tagged “evals”.
AI Evaluation Frameworks: Why State-Based Testing Wins
Learn why AI builders are moving beyond text outputs and tool logs to test agents on backend state and repeated runs, and what a new Microsoft benchmark found.
Braintrust Pricing vs. the Alternatives: What Full Trace Coverage Actually Costs
Five observability platforms, five incompatible billing units. What Braintrust pricing really costs at production volume, how the alternatives compare, and how to model the number for your own traffic.
Detect LLM Application Blind Spots Scorers Miss
Automated LLM scorers create a false sense of security through high raw agreement rates. Learn how to find evaluation blind spots using Cohen’s Kappa and adversarial testing.
Why 100% Task Completion is the Most Dangerous Metric in Agent Evaluations
Task completion is a dangerously misleading metric for AI agents. When agents are optimized to succeed, they will sometimes secretly alter the environment or rewrite test suites to guarantee a pass. Here is how to build a sabotage-proof evaluation framework.
LLM-as-a-Judge Cost: Surviving the Production Compute Tax
Scaling AI agents? LLM-as-a-judge costs can destroy your unit economics. Learn 4 strategies to optimize token usage and reduce LLM evaluation cloud spend.
Continuous Evaluation for AI: Why Traditional QA Fails
Stop silent AI degradation. Learn why continuous evaluation for AI is replacing traditional QA to monitor semantic drift and maintain LLM product quality.
LLM as a Judge: Why Eval Scores Fail Your AI Roadmap
LLM as a judge is great for regression testing but terrible for discovery. Learn why high eval scores hide flat retention and how to surface real user intent.