Guides

How to enhance RAG chatbot performance by refining a reranking model

Learn how to use Labelbox to preprocess documents (e.g., entity detection, document classification, etc) and improve retrieval relevance by fine-tuning a reranking model.

How to build equipment detection models to improve worker safety and efficiency

Learn how you can leverage Labelbox’s platform to build a powerful task-specific model to improve safety detection for personal protective equipment using the latest advances in foundation models to automate labeling.

End-to-end workflow with model distillation for computer vision

Learn how to perform model distillation and fine-tuning to efficiently leverage foundation models for computer vision, like object detection with Amazon Rekognition and YOLOv8.

How to build generative captioning and enrich product listings faster with foundation models

Learn how to leverage Labelbox’s platform and Foundry to build a powerful generative captioning system, ensuring your customers get deeper personalization from LLMs and for your internal teams to derive insights faster from your website product listings.

How to build defect detection models to automate visual quality inspection

Learn how your team can leverage Labelbox’s platform to build a powerful task-specific model to improve defect detection using image segmentation for visual inspection.

How to build defect detection models to improve predictive maintenance

In this guide, we’ll walk through an end-to-end tutorial on how your team can leverage Labelbox’s platform to build a powerful task-specific model to improve defect detection on pipes.

How to build damage classification models with aerial imagery to improve claims automation

Learn how your team can leverage Labelbox’s platform to build a task-specific model to improve building damage detection.

How to Implement Reinforcement Learning from AI Feedback (RLAIF)

The AI revolution is an unstoppable wave, with unique and more capable solutions being rolled out almost every month. These rapid developments, especially around large language models (LLMs), have been made possible by aligning models with human preferences. Reinforcement Learning from AI Feedback (RLAIF) is one of the ways of ensuring such alignments. User feedback has been incorporated into emerging AI models to improve their quality and usefulness. As a result, we have seen more capable model

How to Implement Reinforcement Learning from Human Feedback (RLHF)

The Artificial Intelligence (AI) revolution has been brought to reality with the development of systems and solutions that align with human values and preferences. Reinforcement Learning from Human Feedback (RLHF) is one such example of a system that has transformed model training and improved the accuracy and applicability of AI applications.  Implementing RLHF presents a promising avenue for enhancing AI systems with human guidance. RLHF has been used to develop impressive, human-like convers

How to automatically ingest data from Databricks into Labelbox

Learn how you can leverage Labelbox’s Databricks pipeline creator to automatically ingest data from your Databricks domain into Labelbox for data exploration, curation, labeling, and much more.