Archive: 2026/02
Abstention Policies for Generative AI: When the Model Should Say It Does Not Know
Generative AI often hallucinates answers it can't verify. Abstention policies force models to stay silent when uncertain, reducing harm. Learn how AI learns to say 'I don't know' and why it matters for safety and trust.
Mathematics-Specialized LLMs vs General Models: Accuracy and Cost
Specialized math LLMs like Qwen2.5-Math-7B outperform larger general models like GPT-4 on complex problems while costing far less. RL training is key to balancing accuracy and general capability.
Market Structure of Generative AI: Foundation Models, Platforms, and Apps
Generative AI's market is structured into three layers: foundation models, platforms, and apps. Each plays a distinct role in driving adoption, with vertical apps now outpacing general-purpose tools. Learn how the ecosystem is evolving in 2026.
Data Minimization Strategies for Generative AI: Collect Less, Protect More
Learn how to build powerful generative AI models with less data. Discover practical strategies like synthetic data, differential privacy, and masking to protect privacy without sacrificing performance.
Privacy and Data Governance for Generative AI: Protecting Sensitive Information at Scale
Generative AI is accelerating data leaks, not solving them. Learn how to enforce privacy controls, map AI data flows, and comply with global regulations-before regulators come knocking.
Structured Output Generation in Generative AI: Stop Hallucinations with Schemas
Structured output generation uses schemas to force AI models to return consistent, machine-readable data-eliminating parsing errors and reducing hallucinations in production systems. This is now a standard feature across major AI platforms.
Unit Economics of Large Language Model Features: How Task Type Drives Pricing
LLM pricing isn't one-size-fits-all. Task type-whether it's simple classification or complex reasoning-determines cost. Learn how input, output, and thinking tokens drive pricing, and how smart routing cuts expenses by up to 70%.
Compute Infrastructure for Generative AI: GPUs vs TPUs and Distributed Training Explained
GPUs and TPUs power generative AI, but they work differently. Learn how each handles training, cost, and scaling - and why most organizations use both.
Compliance Controls for Vibe-Coded Systems: SOC 2, ISO 27001, and More
Vibe coding with AI tools like GitHub Copilot is transforming software development - but traditional compliance frameworks like SOC 2 and ISO 27001 can't keep up. Learn the technical controls, industry adoption trends, and real-world risks of AI-generated code compliance in 2026.
On-Prem vs Cloud for Enterprise Coding: Real Trade-Offs and Control Factors
Enterprise teams face real trade-offs when choosing between on-prem and cloud for coding. This article breaks down control, cost, speed, and compliance factors to help teams make intentional deployment decisions-not just follow trends.
Data Extraction and Labeling with LLMs: Turn Unstructured Text into Structured Insights
LLMs are transforming how businesses turn unstructured text into structured data. From contracts to chat logs, automated extraction and labeling cut costs, speed up AI training, and unlock insights at scale.
Chain-of-Thought Prompts for Reasoning Tasks in Large Language Models
Chain-of-thought prompting helps large language models solve complex reasoning tasks by breaking problems into steps. It works best on models over 100 billion parameters and requires no fine-tuning-just well-structured prompts.