Seattle Skeptics on AI

Seattle Skeptics on AI2026-09-19T05:56:16+00:00https://seattleskeptics.org/Tamara Weedhttps://seattleskeptics.org/author/tamara-weed/Measuring Maintainability: Cognitive Complexity and Coupling Metrics

Discover how Cognitive Complexity and coupling metrics like Fan-In/Fan-Out provide a modern approach to measuring software maintainability. Learn why traditional cyclomatic complexity falls short and how to implement these metrics to identify risky code hot spots.

2026-09-19T05:56:16+00:002026-09-19T05:56:16+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/RAG with Vector Databases: Fixing Hallucinations with Embeddings and HNSW

Discover how RAG with vector databases fixes LLM hallucinations. Learn about embeddings, HNSW indexing, and filtering for accurate AI retrieval.

2026-09-18T05:54:12+00:002026-09-18T05:54:12+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Cross-Attention in Encoder-Decoder Transformers: How LLMs Condition on Context

Discover how cross-attention enables encoder-decoder transformers to condition outputs on input context. Learn the mechanics, differences from self-attention, and applications in translation and multimodal AI.

2026-09-17T06:03:44+00:002026-09-17T06:03:44+00:00Science & ResearchTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Multimodal Evolution in Generative AI: 3D, Haptics, and Sensor Fusion

Explore the shift from late fusion to unified multimodal AI. Learn how 3D generation, haptic feedback, and sensor fusion are transforming industries. Discover why unified tokenization is key to efficient, multi-sensory AI systems.

2026-09-16T06:01:19+00:002026-09-16T06:01:19+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Training Data Disclosures for Generative AI: AB 2013 Compliance Guide

California's AB 2013 mandates training data disclosures for generative AI. Learn the 12 required categories, compliance strategies, and how to balance transparency with trade secret protection.

2026-09-15T06:07:15+00:002026-09-15T06:07:15+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Federated Learning for Generative AI: How to Collaborate Without Sharing Data

Discover how federated learning enables privacy-preserving collaboration for Generative AI. Learn how to train models across distributed data sources without sharing raw data, ensuring compliance and security.

2026-09-14T05:56:36+00:002026-09-14T05:56:36+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Safety-Aware Decoding: LLM Guardrails at Inference Time

Discover how safety-aware decoding protects LLMs at inference time. Learn about SafeDecoding, SSD, and ShieldHead techniques that block jailbreaks with minimal latency.

2026-09-13T05:54:43+00:002026-09-13T05:54:43+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Choosing Model Families for Scalable LLM Programs: Practical Guidance

Struggling to pick the right LLM? Learn how to choose between GPT, Claude, Gemini, and Llama based on cost, scale, and specific use cases. Practical guidance for building scalable AI programs in 2026.

2026-09-12T05:56:23+00:002026-09-12T05:56:23+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Generative AI Cost Models: Build vs Buy, Token Pricing, and Infrastructure ROI

Navigate generative AI costs with our guide on build vs buy strategies, token pricing trends, and GPU infrastructure ROI. Learn how to optimize your AI budget in 2026.

2026-09-11T05:53:44+00:002026-09-11T05:53:44+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Benchmarking Bias in Image Generators: Gender and Race Disparities

Discover how diffusion models like Stable Diffusion amplify gender and race biases. Learn why AI image generators skew demographics and what businesses must do to mitigate risks.

2026-09-10T05:53:37+00:002026-09-10T05:53:37+00:00Science & ResearchTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Long-Context Benchmarks for LLMs: Best Evaluation Suites for 2025

Discover the best long-context benchmarks for LLMs in 2025. Compare LongBench Pro, InfiniteBench, and HELM to evaluate model performance on 8k to 1M+ token inputs.

2026-09-09T05:55:22+00:002026-09-09T05:55:22+00:00Science & ResearchTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Reinforcement Learning from Prompts: How RLfP Boosts LLM Quality

Discover how Reinforcement Learning from Prompts (RLfP) automates prompt engineering to boost LLM accuracy by up to 10%. Learn about PRewrite, PRL, costs, and when to use this advanced technique.

2026-09-08T06:01:40+00:002026-09-08T06:01:40+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Query Decomposition: How LLMs Solve Complex Questions

Discover how query decomposition transforms LLM performance on complex questions. Learn why stepwise reasoning beats simple retrieval, the trade-offs in latency, and implementation strategies for enterprise search.

2026-09-07T06:03:00+00:002026-09-07T06:03:00+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Security Architecture for Generative AI: Threat Models and Defenses

Discover how to secure Generative AI systems against unique threats like prompt injection and data poisoning. Learn essential defense-in-depth strategies, access controls, and monitoring techniques to protect your LLM applications.

2026-09-06T05:53:42+00:002026-09-06T05:53:42+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Combining Pruning and Quantization for Maximum LLM Speedups

Discover how combining pruning and quantization boosts LLM speed. Learn about HWPQ, 2:4 sparsity, and practical tips for maximizing model efficiency without losing accuracy.

2026-09-05T05:51:28+00:002026-09-05T05:51:28+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/NLP Evaluation Evolution: Moving from BLEU to LLM-as-a-Judge

Discover why NLP evaluation shifted from BLEU to LLM-as-a-Judge. Learn how semantic metrics and AI judges provide accurate quality assessment for modern language models.

2026-09-04T06:04:16+00:002026-09-04T06:04:16+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Vibe Coding Scaffolds: How AI Builds Initial Architectures from Prompts

Discover how vibe coding transforms natural language prompts into code architectures. Learn the risks, benefits, and best practices for using AI scaffolds without creating technical debt.

2026-09-03T05:52:02+00:002026-09-03T05:52:02+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/State-of-the-Art Transformer Variants for LLMs in 2025

Discover the top transformer variants for LLMs in 2025. Learn how FlashAttention-3, Mamba, and MoE architectures boost speed and context length.

2026-09-02T05:58:41+00:002026-09-02T05:58:41+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Why Opinionated AI Stacks Beat Flexible Ones

Discover why opinionated AI stacks outperform flexible ones in speed and retention. Learn how constrained frameworks reduce decision fatigue and boost productivity.

2026-09-01T06:02:41+00:002026-09-01T06:02:41+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Benchmarking Your Org Against Vibe Coding Leaders: A 2026 Guide

Stop guessing if your AI adoption is working. This guide provides concrete 2026 benchmarks for vibe coding, covering cycle-time reductions, tool accuracy, and governance strategies to help you measure your org against industry leaders.

2026-08-30T06:00:18+00:002026-08-30T06:00:18+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Fairness in Multilingual LLMs: Why English-Centric Alignment Fails

Discover why English-centric alignment fails in multilingual LLMs. Learn how bias shifts across languages and how to audit your models for true global fairness.

2026-08-29T05:54:52+00:002026-08-29T05:54:52+00:00Science & ResearchTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Contrastive Prompting: How to Reduce LLM Hallucinations Without Retraining

Learn how contrastive prompting reduces LLM hallucinations without retraining. Compare Delta, ALCD, and DoLA methods, see performance metrics, and get implementation tips for enterprise AI.

2026-08-28T06:09:12+00:002026-08-28T06:09:12+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Role, Rules, and Context: Structuring Prompts for Enterprise LLM Use

Learn how to structure enterprise LLM prompts using role, rules, and context. Discover advanced techniques like chain-of-thought reasoning and iterative refinement for reliable, scalable AI outputs.

2026-08-27T05:58:11+00:002026-08-27T05:58:11+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Pre-Norm vs Post-Norm Transformers: Stability Guide for LLMs

Discover why Pre-Norm is the standard for stable LLM training. We compare gradient flow, activation risks, and implementation tips for deep Transformer architectures.

2026-08-26T06:00:51+00:002026-08-26T06:00:51+00:00Science & ResearchTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Tokenization in Generative AI: BPE, WordPiece & Beyond

Discover how tokenization shapes generative AI performance. We compare BPE, WordPiece, and emerging strategies to help you optimize costs and accuracy.

2026-08-25T06:00:23+00:002026-08-25T06:00:23+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/PII Detection and Redaction Pipelines for LLM Inputs and Outputs

Learn how to build robust PII detection and redaction pipelines for LLMs. We cover hybrid NER/regex methods, Microsoft Presidio, and compliance strategies to protect user data in production AI deployments.

2026-08-24T05:57:48+00:002026-08-24T05:57:48+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Prompt Robustness: A Practical Guide to Handling Noisy Inputs in LLM Systems

Learn how to handle noisy inputs in LLM systems. We compare MOF, RoP, and PromptBench to help you build robust prompts that survive real-world user errors.

2026-08-23T05:59:27+00:002026-08-23T05:59:27+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Factuality and Faithfulness Metrics for RAG-Enabled Large Language Models: A Practical Guide

Learn how to distinguish factuality from faithfulness in RAG systems. Explore key metrics like context precision, recall, and LLM-as-a-judge approaches to build reliable AI applications.

2026-08-22T05:56:51+00:002026-08-22T05:56:51+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Human-in-the-Loop Review for Generative AI: How to Catch Hallucinations Before Users See Them

Discover how human-in-the-loop review reduces generative AI errors by 58-73%. Learn practical strategies for cost-effective implementation, avoiding reviewer fatigue, and measuring success in high-stakes environments.

2026-08-21T05:51:40+00:002026-08-21T05:51:40+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Bias in Generative AI: How Training Data and Algorithm Design Shape Outcomes

Discover how training data, selection errors, and algorithm design create bias in generative AI. Learn practical strategies for mitigation and the importance of responsible AI development.

2026-08-20T05:50:03+00:002026-08-20T05:50:03+00:00Science & ResearchTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Compression-Aware Prompting: How to Squeeze More Performance from Small LLMs

Learn how compression-aware prompting optimizes small LLMs by reducing token bloat. Discover practical strategies for filtering and distillation to boost accuracy and speed in RAG and local inference.

2026-08-19T05:58:10+00:002026-08-19T05:58:10+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Talent Strategy for Vibe Coding: Roles You Actually Need in 2026

Discover the specific roles and skills required for successful vibe coding adoption in 2026. Learn how to build a hybrid team that balances AI speed with human oversight.

2026-08-18T05:57:22+00:002026-08-18T05:57:22+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Outcome Testing in Vibe Coding: How to Verify Behavior, Not Just Code

Learn how outcome testing transforms quality assurance in vibe coding. Discover why verifying user behavior and experience matters more than inspecting lines of code in AI-assisted development.

2026-08-17T05:58:47+00:002026-08-17T05:58:47+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Runtime Protections for Vibe-Coded Services: WAFs, RASP, and Rate Limits

Vibe coding speeds up development but introduces security risks. Learn how WAFs, RASP, and rate limiting work together to protect AI-generated services from common vulnerabilities.

2026-08-16T05:54:34+00:002026-08-16T05:54:34+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Prompt-Tuning vs Prefix-Tuning: Which Lightweight LLM Method Fits Your Needs?

Compare Prompt-Tuning and Prefix-Tuning for LLMs. Learn which PEFT method saves GPU resources while maximizing accuracy for your specific task.

2026-08-15T05:55:27+00:002026-08-15T05:55:27+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/From BERT to GPT: Understanding the Evolution of Large Language Model Architectures

Explore the architectural evolution from BERT to GPT. Learn how encoder-only and decoder-only designs shape AI capabilities in understanding versus generating text.

2026-08-14T05:54:50+00:002026-08-14T05:54:50+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Architecture Decisions That Reduce LLM Bills Without Sacrificing Quality

Cut LLM costs by 30-80% without losing quality. Learn proven architectural strategies like model routing, semantic caching, and right-sizing to optimize your AI budget effectively.

2026-08-13T05:59:36+00:002026-08-13T05:59:36+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/How Sampling Choices Influence LLM Accuracy: A Guide to Reducing Hallucinations

Discover how sampling choices like temperature and nucleus sampling directly influence LLM accuracy and hallucination rates. Learn practical strategies to optimize your AI outputs.

2026-08-12T05:50:03+00:002026-08-12T05:50:03+00:00Science & ResearchTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Model Parallelism and Pipeline Parallelism: Scaling Large AI Training

Explore how model parallelism and pipeline parallelism enable training of massive AI models by splitting networks across GPUs. Learn about scheduling strategies, hybrid approaches, and real-world implementation challenges.

2026-08-11T06:16:19+00:002026-08-11T06:16:19+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Agentic Systems vs Vibe Coding: Choosing the Right Autonomy Level

Compare vibe coding and agentic systems to choose the right AI autonomy level for your software projects. Learn when to use conversational AI vs autonomous agents.

2026-08-10T05:50:04+00:002026-08-10T05:50:04+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/How to Prompt for Performance Profiling and Optimization Plans: A Developer’s Guide

Learn how to effectively prompt AI for performance profiling and optimization plans. Discover structured techniques to identify bottlenecks, interpret profiler data, and implement actionable code improvements.

2026-08-09T05:51:22+00:002026-08-09T05:51:22+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Product Management for Generative AI: Scoping, MVPs, and Metrics Guide

Master product management for generative AI. Learn how to scope non-deterministic features, define reliable MVPs, and track hybrid metrics that prove business value.

2026-08-08T05:58:41+00:002026-08-08T05:58:41+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Prompt Length vs Output Quality: Why Shorter Prompts Beat Longer Ones in LLMs

Discover why longer prompts often hurt LLM output quality. Learn the science behind attention bottlenecks, recency bias, and how to optimize prompt length for better accuracy and lower costs.

2026-08-07T06:04:29+00:002026-08-07T06:04:29+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Prompting Secure Authentication Flows: OAuth, SSO, and MFA

Learn how to design secure authentication flows using OAuth 2.0, SSO, and MFA. Discover best practices for prompting controls, managing sessions, and balancing security with user experience.

2026-08-06T05:52:55+00:002026-08-06T05:52:55+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Hardware Acceleration for Multimodal Generative AI: GPUs, NPUs, and Edge Devices

Explore how GPUs, NPUs, and edge devices accelerate multimodal generative AI. Learn about hardware constraints, optimization techniques, and the future of unified AI architectures.

2026-08-05T05:56:41+00:002026-08-05T05:56:41+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Safety by Design in Generative AI: Embedding Protections into Product Architecture

Explore Safety by Design in Generative AI, a framework by Thorn and NIST that embeds protections into product architecture to prevent harm like CSAM, moving beyond reactive filters to proactive engineering.

2026-08-04T06:50:58+00:002026-08-04T06:50:58+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Safety by Design in Generative AI: How to Embed Protections into Product Architecture

Explore Safety by Design in Generative AI, a framework by Thorn and partners like NIST that embeds protections into product architecture. Learn how to prevent CSAM and other harms through proactive development, deployment, and maintenance strategies.

2026-08-04T06:50:58+00:002026-08-04T06:50:58+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/Marketing Content at Scale with Generative AI: Product Descriptions, Emails, and Social Posts

Learn how to scale marketing content using generative AI for product descriptions, emails, and social posts. Discover top tools, best practices, and how to maintain brand voice in 2026.

2026-08-03T05:54:40+00:002026-08-03T05:54:40+00:00Enterprise TechnologyTamara Weedhttps://seattleskeptics.org/author/tamara-weed/BERT vs GPT: Choosing Between Encoder-Only and Decoder-Only AI Models

Explore the key differences between BERT and GPT architectures. Learn how encoder-only and decoder-only models impact NLP tasks, performance, and implementation costs.

2026-08-02T05:52:42+00:002026-08-02T05:52:42+00:00Science & ResearchTamara Weedhttps://seattleskeptics.org/author/tamara-weed/BERT vs GPT: Understanding Encoder-Only vs Decoder-Only Architectures

Explore the key differences between BERT and GPT architectures. Learn why encoder-only models excel at understanding while decoder-only models dominate text generation, and how to choose the right one for your NLP project.

2026-08-02T05:52:42+00:002026-08-02T05:52:42+00:00Science & ResearchTamara Weedhttps://seattleskeptics.org/author/tamara-weed/