Few-Shot Example Curation: Quality, Rotation, and Counter-Examples, Part 2
Negative examples sharpen behavior more than positive ones alone.
40 min · Reviewed 2026
The premise
A good example tells the model where to aim; a bad example tells it where the cliff is. Pair them.
What AI does well here
Match the style of provided good examples.
Avoid patterns explicitly marked as bad.
What AI cannot do
Generalize from a single example reliably.
Infer the rule when good and bad look identical.
Anchor Trap: Why Your First AI Example Skews Every Output
The premise
AI weights early examples heavily. If your first sample is verbose, every output gets verbose; if it's casual, all become casual.
What AI does well here
Match the tone, format, and length of your first example.
Repeat structural patterns from samples you provide.
Generalize from 2-3 well-chosen examples.
Detect implicit rules in your demonstrations.
What AI cannot do
Ignore a misleading first example you didn't intend as a pattern.
Average out an outlier sample you put in by accident.
Counter-Example Method: Show AI What 'Wrong' Looks Like
The premise
Showing only good examples teaches the surface; showing good-vs-bad pairs teaches the boundary.
What AI does well here
Learn distinctions from contrasting pairs.
Avoid features marked as bad when generating new outputs.
Match the 'good' side reliably after seeing the contrast.
Calibrate edge cases when you label them.
What AI cannot do
Generalize correctly from a single good/bad pair.
Distinguish stylistic preference from objective error without your labels.
Instructions vs Examples: When AI Needs Each
The premise
Instructions work for tasks AI already knows; examples are needed for novel formats or rare patterns.
What AI does well here
Follow clear instructions for known tasks (translation, summarization).
Generalize from 2-3 examples for unfamiliar formats.
Combine both when neither alone suffices.
Tell you which approach a task likely needs if you ask.
What AI cannot do
Reliably learn novel formats from instructions alone.
Generalize from a single example without context.
AI Few-Shot Example Selection: Which Examples Actually Teach the Model
The premise
Few-shot example quality matters more than quantity — diverse, edge-case examples shape AI behavior, while redundant examples waste context with diminishing returns.
What AI does well here
Generalizing from 3-5 well-chosen examples
Mimicking format, tone, and structure shown in examples
Handling edge cases similar to those demonstrated
Following negative examples when explicitly framed
What AI cannot do
Generalize beyond the distribution shown in examples
Detect that an example set is unrepresentative
AI Zero-Shot Classification: When You Can Skip Few-Shot Examples
The premise
AI zero-shot classification works well when label names carry rich semantics, label sets are small, and classes are clearly distinct — degrading on close-call boundaries.
What AI does well here
Classifying with semantically rich labels like 'urgent customer complaint'
Handling clearly-distinct categories without examples
Producing probability-like confidence when prompted
Following definitions provided per label
What AI cannot do
Distinguish between near-synonym labels reliably
Improve indefinitely without examples on hard boundaries
