Tri-City AI Links - Page 2
Code Execution as a Tool for Large Language Model Agents: How AI Systems Run Code to Solve Real Problems
Code execution lets LLM agents run the code they write, turning them from assistants into active problem-solvers. Learn how GitHub Copilot, CodeWhisperer, and Codey use sandboxing to safely execute code-and why security remains the biggest challenge.
Batched Generation in LLM Serving: How Request Scheduling Shapes Output Speed and Quality
Batched generation in LLM serving boosts efficiency by processing multiple requests at once. How those requests are scheduled determines speed, fairness, and cost. Learn how continuous batching, PagedAttention, and smart scheduling impact output performance.
Few-Shot vs Fine-Tuned Generative AI: How Product Teams Should Choose
Product teams need to choose between few-shot learning and fine-tuning for generative AI. This guide breaks down when to use each based on data, cost, complexity, and speed - with real-world examples and clear decision criteria.
Optimizing Attention Patterns for Domain-Specific Large Language Models
Optimizing attention patterns in domain-specific LLMs improves accuracy by teaching models where to focus within data. LoRA and PEFT methods cut costs and boost performance in healthcare, legal, and finance without full retraining.
Architectural Standards for Vibe-Coded Systems: Reference Implementations
Vibe coding accelerates development but introduces serious risks without architectural discipline. Learn the five non-negotiable standards, reference implementations, and governance practices that separate sustainable AI-built systems from costly failures.
Supply Chain ROI Using Generative AI: Boost Forecast Accuracy and Inventory Turns
Generative AI is transforming supply chains by boosting forecast accuracy by up to 25% and increasing inventory turns through real-time, scenario-based planning. Companies are seeing 200-400% ROI by cutting excess stock and reducing stockouts.
Prompt Chaining vs Agentic Planning: Which LLM Pattern Works for Your Task?
Prompt chaining and agentic planning are two ways to make LLMs handle multi-step tasks. One is simple and cheap. The other is powerful but complex. Learn when to use each-and why most teams should start with chaining.
Pair Reviewing with AI: How Human + Machine Code Reviews Boost Maintainability
AI code review tools boost maintainability by catching bugs early, enforcing consistency, and reducing reviewer fatigue. When paired with human judgment, they speed up PRs, cut technical debt, and keep code clean without replacing expertise.
Prompt Hygiene for Factual Tasks: How to Write Clear LLM Instructions That Don’t Lie
Learn how to write precise LLM instructions that prevent hallucinations, block attacks, and ensure factual accuracy. Prompt hygiene isn’t optional - it’s the foundation of reliable AI in high-stakes fields.
NLP Pipelines vs End-to-End LLMs: When to Use Each for Real-World Applications
Learn when to use traditional NLP pipelines versus end-to-end LLMs for real-world applications. Discover cost, speed, and accuracy trade-offs - and why hybrid systems are becoming the industry standard.
Calibration and Confidence Metrics for Large Language Model Outputs: How to Tell When an AI Is Really Sure
Calibration ensures LLM confidence matches reality. Learn the key metrics like ECE and MCE, why alignment hurts reliability, and how to fix overconfidence without retraining - critical for high-stakes AI use.
Quality Metrics for Generative AI Content: Readability, Accuracy, and Consistency
Learn how to measure the quality of AI-generated content using readability, accuracy, and consistency metrics. Avoid misinformation, improve user trust, and build reliable AI workflows with proven tools and real-world examples.
