Only 14% of generative AI proof of concepts make it to production. Learn how to bridge the gap with a structured approach that prioritizes business outcomes, security, and real-world reliability over flashy demos.
RLHF and supervised fine-tuning are the two main ways to align LLMs with human needs. SFT is fast and accurate for structured tasks. RLHF makes models feel human-but at a high cost. Here’s how to choose the right one.
Red teaming prompts for generative AI uncovers hidden safety gaps by simulating attacker behavior. Learn how to find jailbreaks, data leaks, and prompt injections before they're exploited.
Security telemetry for AI-generated apps tracks how models think, not just what they do. Learn how to detect prompt injections, model drift, and data poisoning before they cause damage.
Enterprise LLM guardrails prevent data leaks, ensure compliance, and block harmful outputs. Learn how to design, approve, and deploy multi-layered guardrails using proven frameworks and real-world benchmarks.
Learn how major LLM providers handle updates and deprecations, why lifecycle management matters, and what steps you need to take today to avoid costly AI failures. Understand the real differences between open-source and proprietary models.
A successful generative AI system isn't about the model-it's about data quality, smart orchestration, and reliable infrastructure. Learn what actually works in production in 2025.