Tutorials on Compliance

Long-term monitoring of user behavior in large language models (LLMs) is about tracking how users interact with AI systems over months or years. This approach helps identify trends, system performance issues, and user needs that short-term testing often misses. Key focus areas include: The goal is to ensure LLMs remain reliable, cost-effective, and user-focused by using data-driven insights to guide improvements. To effectively monitor how users interact with large language models (LLMs), it’s essential to focus on core performance indicators that reflect the system's ability to meet user needs. Start by evaluating response accuracy - this means checking if the answers provided are contextually relevant, factually correct, and aligned with the user's intent.

Explore the differences between post-hoc and built-in explainability methods in large language models, and their implications for trust and compliance.

Explore the differences between AI agents and chatbots in HR recruitment, their benefits, drawbacks, and how to choose the right tool for your needs.

Learn essential preprocessing steps for multilingual data to enhance fine-tuning of language models and ensure quality, diversity, and compliance.

Explore the critical differences between LLM monitoring and traditional logging in AI systems, focusing on output quality, safety, and compliance.

Explore the privacy risks and protections of Retrieval-Augmented Generation systems, focusing on data security in sensitive industries.

Learn how to effectively monitor large language models with dashboards that track performance, quality, costs, and compliance.

Transform generic AI models into specialized tools for customer support through fine-tuning, enhancing accuracy, efficiency, and compliance.

Learn how horizontal scaling optimizes large language model deployments by improving efficiency, cost management, and fault tolerance.

Explore essential strategies for securing AI workflows against data breaches, insider threats, and compliance challenges in a rapidly evolving landscape.