Insights, tips, and product updates

What's the difference between LLM-as-a-judge, HITL, and HOTL workflows? We cover this and provide practical tips for each application in our latest guide.

Vision models segment almost anything except the defects and targets specialists care about. This guide to fine-tuning vision models, from SAM and DINO to VLMs, shows how expert masks close the gap.

Published word error rates come from read audiobooks, close-talk meeting headsets and telephone corpora three decades old. This guide takes apart the distance between those numbers and the transcript of an actual customer call: what WER counts, what it forgives before counting begins, and what it cannot see.

LLM regression testing checks, item by item, whether a model update fixed more than it broke on a fixed expert-graded holdout. Learn how to catch regressions, compare two model versions fairly and tell when a difference is real.

Inter-rater reliability tells you whether your experts' labels can be trusted before they grade a model or train one. This guide covers percent agreement, Cohen's kappa, Fleiss' kappa, Krippendorff's alpha, gold answers and the thresholds to set.

Error analysis tells an LLM team what its product gets wrong before it picks metrics or judges. How to code traces into a failure taxonomy, who should read them, and when to build a review tool.

Supervised fine-tuning uses thousands of examples, so each expert-written answer shapes the model. How to qualify writers, set guidelines and length limits, review answers and format the SFT dataset.

Model distillation trains a small, cheap model on the outputs of a larger one, and the student inherits the teacher's errors along with its skills. Learn how knowledge distillation works and where to check the teacher's outputs before they become training data.

Data quality management is where most enterprise AI projects quietly fail. The teams that treat annotation as an engineering discipline, not an afterthought, are the ones shipping models that work. This guide breaks down the operational practices that produce high quality data at scale, drawing on recent research from Stanford, McKinsey, Google DeepMind, and peer-reviewed annotation science.

Domain experts agree only when the guideline, the rubric and the class list give them one standard to apply. How to write annotation guidelines and evaluation rubrics, design the ontology, pilot it and version it.

This guide will walk you through everything you need to know about OCR data labeling, from understanding the fundamentals to implementing quality workflows that scale across your organization.

Learn how modern data labeling combines automated labeling and expert HITL workflows to embed subject-matter expertise throughout the AI lifecycle, improving data quality, scalability, and model performance in production.

Assessing and benchmarking LLMs makes it easier for data science teams to select the right model and develop a strategy to adapt it faster. Here's a guide to building an LLM evaluation dataset.

RLHF is now one stage in a three-stage post-training pipeline, and each stage asks people for a different kind of judgment. This guide maps SFT, preference optimisation and verifiable rewards by the human work they need, and by how each one fails when that work is weak.

Fine-tuning and aligning LLMs can be a challenging and complex process. In this article, we hope to clarify and structure this journey based on our experience with clients and existing examples.

Poor data quality can have significant and far-reaching consequences across various industries and applications. But how do we measure data quality? What metrics can we use to ensure the quality of our labeled data remains of the highest standard?

Discover how to tackle the biggest challenges of satellite imagery and geospatial data annotation. Learn which tools are available to you.

Unlock the full potential of satellite imagery with advanced approaches to Earth Observation data labeling.

Despite the critical role it plays in the success of labeling projects, building efficient labeling guidelines can be very difficult.  This blog will present the general approach and provide tips based on Kili Technology’s expertise in the construction of such guidelines.

Whether you're building training data for a cutting-edge autonomous system or developing retail analytics, video annotation is the foundation of computer vision success. The right combination of skilled annotators, efficient video annotation tools, and robust processes will help you create the accurate video annotations your AI models need to perform in real world applications.