Tri-City AI Links
Zero-Shot vs Few-Shot Learning: When to Use Examples in LLMs
Learn the difference between zero-shot and few-shot learning in LLMs. Discover when to use examples to improve AI accuracy and how to choose the right approach for your project.
Legal AI Safety: How to Avoid Hallucinations After Mata v. Avianca
Learn from the Mata v. Avianca disaster to build AI safety policies that prevent hallucinations and legal sanctions in professional workflows.
LLM Risk Management: Technical Controls and Escalation Paths for AI Governance
Learn how to manage LLM risks with dynamic controls, behavioral guardrails, and clear escalation paths to ensure AI governance and safety.
Evaluating RAG Pipelines: Mastering Recall, Precision, and Faithfulness
Learn how to evaluate RAG pipelines using recall, precision, and faithfulness. Master the metrics needed to stop LLM hallucinations and improve retrieval quality.
Debugging Prompts: Systematic Methods to Improve LLM Outputs
Learn systematic methods to debug and improve LLM outputs, from task decomposition and RAG to advanced mathematical steering and prompt chaining.
Differential Privacy in LLM Training: Balancing Data Protection and Model Performance
Explore how Differential Privacy protects sensitive data in LLM training. Learn about DP-SGD, the epsilon-delta tradeoff, and how to balance privacy with model accuracy.
COPPA and Generative AI: Navigating Children's Data Privacy Rules
Learn how the 2025-2026 COPPA updates change data collection for Generative AI. Discover new rules on parental consent, biometrics, and data retention to avoid FTC penalties.
MoE Architectures: Balancing Cost and Quality in Large Language Models
Explore the trade-offs of Mixture-of-Experts (MoE) in LLMs. Learn how sparse activation reduces compute costs while increasing memory demands for better AI scale.
Building PII Detection and Redaction Pipelines for LLMs
Learn how to build PII detection and redaction pipelines for LLMs using hybrid Regex/NER methods and tools like Microsoft Presidio to ensure data privacy.
Multimodal Evolution in Generative AI: 3D, Haptics, and Sensor Fusion
Discover how AI is evolving from late fusion to unified architectures. We explore the rise of 3D, haptics, and sensor fusion in 2026.
Bias in Generative AI: How Training Data, Selection, and Algorithmic Design Shape Outcomes
Explore how training data selection and algorithm design drive bias in generative AI. Learn about real-world impacts, mitigation techniques like the MIT method, and practical steps to reduce discrimination.
Red Teaming Prompts for Generative AI: Finding Safety and Security Gaps
Learn how to identify and fix safety gaps in generative AI using red teaming strategies. Covers prompt injection, automation tools, and regulatory compliance.
