AI & Software

Prompt engineering feels like skill until a model update quietly invalidates your work. Here's why that happens and what to do about it.

Retrieval-Augmented Generation is genuinely useful, but it's solving a narrower problem than most teams think. Here's what it actually does and doesn't fix.

Bigger context windows don't mean better memory. They introduce a specific failure mode where models systematically ignore the middle of what you give them.

Large language models output certainty as a stylistic default. Here's why that's a harder problem than it sounds, and what it means for trusting AI output.

Code-generating AI is trained on text, not execution. Understanding that gap changes how you should use it.

LLMs don't crash when they hit unknown territory. They improvise, and understanding how reveals a lot about when to trust them.

AI coding assistants generate plausible-looking code but have no mechanism to verify correctness. That's not a bug to be patched. It's structural.

The context window isn't just a text bucket. Understanding how models use that space changes how you work with them.

AI coding tools are genuinely useful. They're also structurally blind to whether the code they produce actually does what you need. Here's what that means for your workflow.

Better reasoning doesn't fix hallucination. It makes the model better at sounding right while being wrong.

AI models update without announcements, and your carefully tuned prompts may stop working the moment the model underneath them changes.

Giving an LLM access to 200,000 tokens doesn't mean it actually attends to all of them. Here's why context window size and context utilization are different problems.

Code generation models predict plausible syntax, not working software. Understanding that distinction changes how you should use them.

That percentage your AI tool displays isn't a real probability. It's a number that looks trustworthy because numbers look trustworthy.

The AI industry is shipping powerful tools with the same blind spots that haunted web apps in the early 2000s. We should know better by now.

AI summarizers are fluent and fast, but they optimize for what's statistically central, not what's actually important. Here's what that costs you.

If a bug only shows up once and then vanishes, that's not luck. That's a system telling you something about its own hidden complexity.

Between your words and the model's attention lies a pipeline that silently rewrites your request. Understanding it changes how you work with AI.