Tri-City AI Links
Model Parallelism and Pipeline Parallelism in Large Generative AI Training
Pipeline parallelism enables training of massive generative AI models by splitting them across GPUs, overcoming memory limits. Learn how it works, why it's essential, and how it compares to other parallelization methods.
Security Operations with LLMs: Log Triage and Incident Narrative Generation
LLMs are transforming SOC operations by automating log triage and generating clear incident narratives, reducing alert fatigue and response times. Learn how they work, their real-world accuracy, risks, and why humans still must stay in the loop.
Citation Strategies for Generative AI: How to Link Claims to Source Documents Without Falling for Hallucinations
Generative AI can't be trusted as a source. Learn how to cite AI tools responsibly, avoid hallucinated facts, and verify claims using real sources-without risking your academic integrity.
RAG System Design for Generative AI: Mastering Indexing, Chunking, and Relevance Scoring
RAG systems reduce AI hallucinations by retrieving real-time data instead of relying on static training. Learn how indexing, chunking, and relevance scoring make RAG accurate, reliable, and enterprise-ready.
Code Generation with Large Language Models: How Much Time Do You Really Save?
AI code generators like GitHub Copilot save developers hours on routine tasks but introduce hidden risks in security and correctness. Learn where they excel, where they fail, and how to use them safely.
Domain-Specific RAG: Building Compliant Knowledge Bases for Regulated Industries
Domain-specific RAG transforms compliance in regulated industries by grounding AI in verified regulations. Learn how healthcare, finance, and legal teams use it to cut errors, speed up audits, and stay compliant-with real data from 2025 deployments.
Preventing RCE in AI-Generated Code: How to Stop Deserialization and Input Validation Attacks
AI-generated code often contains dangerous deserialization flaws that lead to remote code execution. Learn how to prevent RCE by replacing unsafe formats like pickle with JSON, validating inputs, and securing your AI prompts.
Performance Budgets for Frontend Development: Set, Measure, Enforce
Performance budgets set hard limits on page weight, load time, and JavaScript to prevent slow frontends. Learn how to set, measure, and enforce them using Lighthouse CI, Webpack, and Core Web Vitals for real user impact.
Tempo Labs and Base44: The Two AI Coding Platforms Changing How Teams Build Apps
Tempo Labs and Base44 are leading the rise of vibe coding-AI platforms that turn natural language into working apps. See how they differ, who they're for, and which one fits your team in 2026.
How to Manage Latency in RAG Pipelines for Production LLM Systems
Learn how to reduce latency in production RAG pipelines using Agentic RAG, streaming, batching, and vector database optimization. Real-world benchmarks and fixes for sub-1.5s response times.
Explainability in Generative AI: How to Communicate Limitations and Known Failure Modes
Generative AI can make dangerous mistakes-but explaining why is harder than ever. Learn how to communicate its known failure modes, from hallucinations to bias, and build accountability without false promises.
Product Management for Generative AI Features: Scoping, MVPs, and Metrics
Managing generative AI features requires a new approach to scoping, MVPs, and metrics. Learn how to avoid common pitfalls, build capability-based tracks, and measure real user impact-not just clicks.
