field-notes
Three defenses against confabulation
Three soft layers between a user and hallucination all fail silently. Three hard defenses make confabulation visible, measurable, and refusable without suppressing the answer.
Tagged: ai-transparency
7 entries
weblinks
State of RAG & GenAI
This article argues that Retrieval Augmented Generation (RAG) has become a strategic imperative for enterprises in 2026, addressing critical challenges like LLM hallucinations, outdated outputs, and high retraining costs. RAG bridges the gap between large language models and organizational knowledge by retrieving verified, real-time data at the moment of generation, ensuring outputs are accurate, compliant, and trustworthy without requiring constant model retraining.
weblinks
Oh-My-Nav
This is a documentation hub for GitHub Copilot adoption and best practices at NAV (the Norwegian Labour and Welfare Administration). The site provides news updates, tools, guidelines, and usage statistics for AI-driven development, focusing on custom agents, skills, and organizational implementation of GitHub Copilot across teams.
weblinks
Don't show me your AI. It is rude!
Marek Tuszynski (Tactical Tech) argues that the synthetic intimacy people develop with generative AI tools (an 'ELIZA effect') makes critical conversation about AI almost impossible: dependency forecloses critique. The piece curates 30 resources mapping AI's labor exploitation, environmental costs, military use, and concentration of power, framing AI critique as a moral and political project rather than a technical one.
experiments
Hallucinated library entry: post-mortem and safeguard
How Claude invented a book by my competitor, why it happened, and the rule added to prevent it.
seeds
Style guide for AI
Language rules for how I talk about AI in my work.
seeds
AI transparency in content
How should I indicate when content was made with AI, and share the prompts?