Domain-specific LLM agents are transforming IT support by automatically categorizing, linking, and resolving tickets with 95% accuracy. They cut resolution time by 30%, reduce agent workload, and handle 1 in 5 tickets without human help.
Vibe coding with LLMs may feel fast, but it often generates insecure code. Learn the anti-pattern prompts to avoid and how to write secure, structured prompts that prevent vulnerabilities before they happen.
Batched generation in LLM serving boosts efficiency by processing multiple requests at once. How those requests are scheduled-using continuous batching, PagedAttention, and learning-to-rank algorithms-directly impacts throughput, latency, and cost. This is how top systems like vLLM make it work.
AI refactoring can silently break app security. Learn how security regression testing catches hidden vulnerabilities in AI-generated code, why standard tests fail, and how to implement it now with proven tools and strategies.
Smaller LLMs can learn complex reasoning by copying the step-by-step thought processes of larger models. This technique, called chain-of-thought distillation, cuts costs by 90% while keeping most of the accuracy - but comes with hidden risks.
NLP pipelines and LLMs aren't competitors-they're partners. Learn when to use rule-based systems for speed and cost, and when to let large language models handle complex reasoning-without blowing your budget.
Generative AI requires formal impact assessments under GDPR and the EU AI Act. Learn what DPIAs and FRIAs are, when they're mandatory, which templates to use, and how to avoid costly fines in 2026.
Style guides ensure consistent code across teams and sessions, reducing review time, cutting bugs, and making onboarding faster. Learn how to build one that works without driving developers crazy.
Security KPIs for LLM programs measure real risks like prompt injection, data leakage, and model abuse. Learn the key metrics, benchmarks, and implementation steps to protect your AI systems from emerging threats in 2026.
Rollback playbooks are essential for quickly recovering from AI deployment failures. Learn how top companies use canary releases, feature flags, and automated triggers to prevent costly AI errors and meet regulatory requirements.
Pipeline parallelism enables training of massive AI models by splitting them across GPUs, overcoming memory limits that single devices can't handle. Learn how it works, why it's essential, and what's new in 2026.
Global LLM deployments must comply with data residency laws like GDPR and PIPL. Learn how hybrid architectures, SLMs, and local infrastructure help avoid fines while maintaining AI performance.