Field notes from a journey to AI.
Long-form posts written from a working professional's seat - what's actually useful, what's hype, and how to bring AI back to your team on Monday.
I kept getting generic answers. Few-shot prompting fixed it.
Few-shot prompting uses worked examples to guide AI output. Learn when zero-shot, one-shot, or few-shot gets you a precise answer without repeated tries.
Chain of thought prompting changed how I read AI answers.
Chain-of-thought prompting turns an LLM into a deliberate reasoner. Learn why forcing intermediate steps reduces AI errors and how to design the chain.
The AI had everything it needed and still got it wrong.
Feed AI too much context and it loses focus on what matters. That is context overload. Here is why it happens and how scope reduction fixes it.
I asked for a phone deal. The AI invented the phone.
AI hallucination causes explained: five conditions that make your AI fabricate confidently, and two prompt habits that cut it before the output lands.
I asked AI to fix 120 pages. The false positives were on me.
Context engineering vs prompt engineering, told through a 120-page proofread that came back in five minutes with false positives I had to throw out.
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