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Thinking out loud from inside the lab.
Technical writing, research findings, opinions, and tutorials from the Karat Labs team. No marketing, just thinking.
Why Eval Harnesses Are the Most Underrated Part of LLM Engineering
Most teams ship AI products without any systematic way to measure quality. Here is why evaluation infrastructure should be the first thing you build, not the last.
Building RAG Pipelines That Actually Work in Production
Retrieval-Augmented Generation sounds simple in theory. In practice, it requires careful engineering of embedding pipelines, chunking strategies, and retrieval quality metrics.
The Case for Agent Reliability Engineering
Autonomous agents fail in ways that are fundamentally different from traditional software. Here is how we think about reliability in multi-step AI systems.
Fine-Tuning vs Prompt Engineering: When to Use Which
Not every problem needs fine-tuning, and not every problem can be solved with prompt engineering alone. A practical framework for deciding.
Measuring Hallucination Rates in Production LLM Systems
Hallucination detection is not a binary problem. We break down approaches from simple heuristics to model-graded evaluation, with real production numbers.
The MLOps Stack for Small AI Teams
You do not need a massive infrastructure team to run AI in production. Here is the minimal viable MLOps stack we recommend for teams of 3 to 10 engineers.
Red Teaming Your Own AI: A Practical Guide
If you are not actively trying to break your AI system, someone else will. A step-by-step approach to adversarial testing for production AI.
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