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Artificial intelligence (AI) LLM

How to Actually Abuse LLMs

Understanding an LLM’s edges matters more than flattering it.
This post explores how deliberately pushing models into conflicting, noisy, or chained prompts reveals their failure modes — and why learning those edges is the core of real prompt engineering.

By michal, 3 weeksFebruary 14, 2026 ago
Artificial intelligence (AI) LLM

Why LLMs Hallucinate — Patterns, Pitfalls, and How to Guard Against It

Hallucination isn’t a mystery glitch — it’s a predictable consequence of how LLMs work. This Field Note breaks down why models fabricate information, the patterns behind it, and practical ways to reason about and mitigate hallucination in real systems.

By michal, 4 weeksFebruary 7, 2026 ago
Artificial intelligence (AI) LLM RAG

How to Build Reliable LLM Pipelines — Grounding, Verification, and Resilience

LLM pipelines aren’t just prompts and models — they’re grounded, verified, cached, and resilient systems. This Field Note breaks down pragmatic patterns like retrieval grounding, verification checks, caching strategies, and reliability practices that keep AI systems dependable beyond demos.

By michal, 1 monthFebruary 1, 2026 ago
Recent Posts
  • Claude Code — The Practical Guide to Agentic Coding
  • AI Fluency Explained: From Simple Prompts to Autonomous Agents
  • How to Actually Abuse LLMs
  • Why LLMs Hallucinate — Patterns, Pitfalls, and How to Guard Against It
  • How to Build Reliable LLM Pipelines — Grounding, Verification, and Resilience
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