AI Capabilities and Limitations
AI is not uniformly capable or uniformly unreliable. It is strong and weak along specific, predictable axes.
I teach the words before the tools: hallucination, sycophancy, automation bias. People stop being surprised once they have the names.
Based on the AI capabilities and limitations course by the Anthropic team. Thirteen lessons and a quiz, listed at 3.5 hours.
AI Fluency teaches the human side, the four Ds. This page teaches the machine properties those four Ds are responding to: AI Fluency →
Next token prediction
Where the answers actually come from
The model is not looking things up. It composes the answer word by word, based on what tends to follow what.
Closer to an extraordinarily sophisticated autocomplete than to a search engine.
- Is this a well-worn path, or out near an edge?
- How precise is the claim: a name, a date, a figure, a citation?
- Would you notice if it were wrong?
A confident tone does not signal accuracy. Specificity is where fabrication concentrates, so check the names, dates, statistics, citations and URLs.
Knowledge
What it actually knows, and what it cannot
It knows what it read in training, and only that.
Training ends on a date, and everything after that moment simply is not there.
- How well represented was this in what it read?
- Is the answer time sensitive: current events, who holds a job, what something costs?
- Can it tell you where that came from?
Verify anything time sensitive, and test before you trust in a new domain. Brilliance in one area does not transfer to the one next door. Search and retrieval tools are there specifically to patch these gaps.
Working memory
What it is paying attention to right now
Everything sits inside one fixed-size workspace called the context window.
When it overflows, the oldest material usually falls off, and usually in silence.
- Has this conversation run long, and has the quality started to slip?
- Is the part that matters buried in the middle of a long document?
- Are you expecting it to remember a session it never saw?
This one has a cliff. Lead with what matters, chunk long work into passes, and start fresh when quality degrades. A new chat with a short summary can outperform pushing through.
Steerability
How much control your instructions give you
Specify a role, a tone, a format, a word limit, and the model applies them, often on the first try.
But steerability is not the same thing as understanding.
- How much room is there between what you typed and what you actually want?
- Is the instruction short, concrete and checkable?
- Could a small error in step two carry quietly through steps three and four?
It honours what you said and misses what you meant. When that happens, restate the goal rather than the instruction. Short and checkable beats long and ambiguous.
Name the two properties before you touch the prompt
Most real surprises are not one property failing. They are two meeting at once, and naming which two makes the fix obvious.
- 1
Say what was actually off. Not “that was not quite right”, but which part, and how.
- 2
A citation that does not exist? Prediction meeting a knowledge gap. Verify the specifics, or use a tool that grounds them in real sources.
- 3
Your constraints ignored twenty messages in? Working memory meeting steerability. Resupply the essentials, or start fresh with them up front.
- 4
Only then rewrite the prompt. Jump straight there and you are guessing.
Ask it before you start too: which properties am I looking at here? More of the same triage sits in the FAQ →
Place the task, then decide
Safe handoff
a well-worn path- Summarising, reformatting, explaining.
- Mainstream topics, before the cutoff.
Check it
thin ground- Niche, recent or local.
- Any precise name, date or figure.
Bring your own
outside its reach- Your documents, your numbers.
- Upload it, or give it a search tool.
The goal is not to distrust AI, and not to hand it everything. It is calibrated trust, neither granting it nor withholding it wholesale.
- Next token prediction: it writes what comes next, it does not look things up.
- Knowledge: broad, uneven, and frozen at a cutoff.
- Working memory: one fixed window, and this one has a cliff.
- Steerability: it follows your words, not your intent.
- When they collide: name the two, then pick the fix.
This week: take one answer that annoyed you and name which two properties caused it.
The human half of the same picture: AI Fluency → Close the gap between what you type and what you mean: How to Prompt →
Do it with someone. Everything here is free and you can run it alone. If you would rather work through it with me, one to one, book a free 30-minute chat. No pitch.