LLM Evaluation Insights

RAG has two places to fail: retrieval and generation. Learn how to measure context precision, faithfulness, and answer relevance — and how to catch the silent failures that only appear in production.

Prompt changes can shift output quality in ways that aren't obvious until you measure them. Here's how to run controlled A/B tests on your LLM prompts using evaluation data instead of user complaints.

When an LLM provider updates their model, output quality silently degrades. Learn concrete detection strategies: baseline scoring, automated regression tests, shadow scoring, and alert thresholds.

Token costs are falling across every major provider. Here's what's changed, which models offer the best price-to-quality ratio right now, and how to structure your LLM spend for the rest of 2026.

Benchmark pricing is one thing. What you actually pay after accounting for token overhead, retry rates, and output length variance is another. Real cost data across 12 models.

Accuracy, latency, cost — but what else? Here are the five evaluation metrics that separate solid LLM integrations from ones that quietly degrade over time.

Unit tests catch bugs in deterministic code. LLMs are not deterministic. Here's why your existing test strategy fails for AI features and what to replace it with.

Your LLM-powered feature worked perfectly last week. This week, users are complaining. You haven't touched the code. The provider's changelog says nothing changed. But something did.