Tri-City AI Links

How to Structure Generative AI Outputs into JSON and Tables: A Practical Guide

Learn how to craft effective data extraction prompts for Generative AI. This guide covers structuring outputs into JSON and tables, avoiding common pitfalls, and implementing validation for accurate results.

Vibe Coding Explained: Who Can Build Software Now in 2026

Discover how vibe coding democratizes software development by allowing anyone to build apps using natural language. Learn who can benefit, the risks involved, and how to get started in 2026.

The Future of Generative AI: Agentic Systems, Lower Costs, and Better Grounding

Explore the 2026 trajectory of Generative AI: the rise of autonomous agentic systems, cost reductions via synthetic data, and enhanced accuracy through RAG grounding.

Compress or Switch? A Practical Guide to Optimizing LLM Systems

Decide whether to compress or switch your LLM. Learn when quantization saves money and when switching to smaller models prevents performance loss.

Key, Query, and Value Projections in LLM Attention: What the Matrices Learn

Explore how Key, Query, and Value matrices power LLM attention. We break down the math, the database analogy, and what these projections actually learn during training.

How Multimodal LLMs Understand Speech and Audio in 2026

Explore how Multimodal LLMs achieve speech and audio understanding in 2026. Learn about spectrogram processing, SALM architectures, and real-world performance benchmarks.

Transformer Efficiency Tricks: KV Caching and Continuous Batching in LLM Serving

Learn how KV caching and continuous batching optimize LLM serving. Explore memory trade-offs, vLLM implementation, and compression techniques like NVFP4 for faster inference.

Zero-Trust Architecture for Large Language Model Integrations: A Security Guide

Learn how to secure AI with Zero-Trust Architecture for LLMs. Discover guardrails, sentinel systems, and data protection strategies to prevent breaches and ensure compliance.

Error Analysis for Prompts in Generative AI: Diagnosing Failures and Fixes

Stop guessing why your AI fails. Learn systematic error analysis to diagnose prompt failures, reduce hallucinations by up to 60%, and build reliable generative AI systems with proven metrics.

Modularizing AI-Generated Logic: Extract, Isolate, and Simplify

Learn how to improve AI maintainability by modularizing logic. Discover strategies to extract, isolate, and simplify AI systems using MRKL and neuro-symbolic approaches for better reliability.

Healthcare Vibe Coding: Safe Prototyping Without PHI in 2026

Explore how vibe coding enables healthcare professionals to build safe prototypes without exposing PHI. Learn about AI tools, compliance strategies, and real-world implementation tips for 2026.