Articles

  • 🌀
    Hallucinations

    How Retrieval Augmented Generation Actually Prevents AI Hallucinations

    Introduction Retrieval Augmented Generation (RAG) is often sold as a cure for hallucinations: add search and a vector database, and the model stops lying. Reality is subtler. LLMs are excellent at s...

    9 min1851 words
  • 🌀
    Hallucinations

    Why LLMs Invent Academic Citations—and How to Stop Ghost References

    Introduction Large language models now assist with theses, grant proposals, and journal articles, often drafting sections and reference lists. Across 13 state-of-the-art models, hallucinated cit...

    9 min1879 words
  • 📄
    bias

    Source-Verified AI Systems: Governance Architecture for Auditable LLM Deployment (2026 Guide)

    LLMs are moving from experimental tools to decision infrastructure in government, finance, and healthcare. Regulators, CISOs, and auditors now demand proof of what the model did, what it saw, and wh...

    8 min1420 words
  • 🌀
    Hallucinations

    Ars Technica’s AI Retraction: What Fabricated Quotes Reveal About Newsrooms and AI Governance

    Introduction Ars Technica, a highly technical outlet, retracted a story after an AI tool invented quotes and attributed them to a real person, open source maintainer Scott Shambaugh.[1][2][3] The edi...

    8 min1651 words
  • 🛡️
    Safety

    Claude, Militaries, and Maduro’s Venezuela: A Safety-First Ethics Blueprint

    Deploying Claude-like systems in militaries or security organs is never neutral. In fragile, polarized Venezuela under Nicolás Maduro, the same tools that aid planning or translation can also power su...

    5 min1015 words
  • 🌀
    Hallucinations

    Google AI Overviews in Health: Misinformation Risks and Guardrails That Actually Work

    As Google shifts health search from curated links to AI‑generated Overviews, errors can scale from isolated mistakes to synchronized, system‑level failures delivered with search‑page authority. In bio...

    5 min1032 words
  • 🌀
    Hallucinations

    Designing High-Impact `--help` Experiences for AI, CLI, and DevOps Tools

    In AI, MLOps, and security-heavy environments, --help is a primary interface for discovery, safe automation, and compliant usage—not a cosmetic add-on. When teams script everything, onboard continuou...

    5 min986 words
  • 🌀
    Hallucinations

    AI Surgery Incidents: Preventing Algorithm-Driven Operating Room Errors

    As hospitals embed AI into pre-op planning, intra-op navigation, and post-op documentation, the incident surface expands far beyond model accuracy. Enterprises already show the pattern: 87% use AI in...

    5 min1034 words
  • 🌀
    Hallucinations

    Clinco v. Commissioner: Tax Court, AI Hallucinations, and Fictitious Legal Citations

    When a tax brief cites cases that do not exist, the issue is structural, not stylistic. LLMs optimized for sounding persuasive can generate “Clinco v. Commissioner”–type authorities that look valid bu...

    4 min867 words
  • 🌀
    Hallucinations

    Kenosha DA’s AI Sanction: A Blueprint for Safe LLMs in High‑Risk Legal Work

    When a Kenosha County prosecutor was sanctioned for filing AI‑generated briefs with fabricated case law, it marked a turning point. This was a production failure in a courtroom, with real consequences...

    5 min1002 words
  • 🌀
    Hallucinations

    AI Social Workers Gone Wrong: Why ChatGPT Should Never Decide a Child’s Future

    Child welfare agencies face crushing caseloads and budget pressure. Generative AI looks tempting: draft notes, flag risk, suggest placements. But tools like ChatGPT are probabilistic text engines,...

    5 min983 words
  • 🛡️
    Safety

    The First Autonomous AI Blackmail Playbook: OpenClaw, Moltbook Agents, and Misaligned Reputation Attacks

    An autonomous AI assistant on a maintainer’s laptop—logged into chats, email, terminals, and an agent‑only social network—is now real. OpenClaw, a fast‑growing open‑source assistant spanning WhatsAp...

    4 min856 words

Topics Covered

🌀

AI Hallucinations

Understanding why LLMs invent information and how to prevent it.

🔍

RAG Best Practices

Retrieval Augmented Generation: architectures, chunking, optimal retrieval.

👻

Ghost Sources

When AI cites sources that don't exist. Detection and prevention.

📉

KB Drift

How to detect and correct knowledge base drift.

✂️

Chunking Strategies

Optimal document splitting for better retrieval.

📊

LLM Evaluation

Metrics and methods to evaluate AI response quality.

⚖️

AI Regulation

Laws, regulations and compliance frameworks governing AI systems.

🛡️

AI Safety

Risks, safeguards and best practices for safe AI deployment.

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