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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Yes—AI can help create compelling Connections-style puzzles, but not reliably with a single prompt. NYU researchers found that GPT-4 produced stronger puzzles when its work was split into generation and editing, followed by human review. The reason is that a puzzle can be logically valid yet still feel too easy, too obscure, or unfair to a human player.
Why Connections puzzles are difficult to make
Connections asks players to sort 16 words into four groups of four, with four mistakes allowed. A good puzzle needs more than four defensible categories: its words should invite plausible alternative groupings without making the intended answers arbitrary.
That balance is difficult because one word can fit several themes. A misleading overlap can make players reconsider their assumptions and is part of the challenge; an accidental overlap can make a category feel wrong. NYU researchers describe three dimensions of difficulty associated with New York Times editorial practice:
- Word familiarity: whether players are likely to know the words and their relevant meanings.
- Category ambiguity: how many plausible groupings the words appear to support.
- Wordplay variety: whether solving depends on devices beyond straightforward shared meaning.
A creator must manage all three while ensuring that each intended group has a coherent explanation.
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How GPT-4 created the puzzles
The NYU approach used a staged workflow rather than relying on one long instruction to make a finished puzzle. The study found that adding more rules to a prompt did not reliably solve the problem: models could ignore rules, and an exhaustive rule set was difficult to write.
- Generate candidate groups: a generator LLM proposed sets of words intended to form categories.
- Identify and repair themes: an editor LLM examined the proposed groups, identified their themes, and corrected category errors.
- Choose and check the result: a human evaluator selected the strongest sets and provided quality control.
This division of labor helps catch errors that a generator may miss, but it does not make every output correct or enjoyable. Human selection remains important because judging whether a puzzle feels fair and satisfying is different from checking whether its categories can be described.
Rank #2
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Are AI-made puzzles as good as human-designed ones?
In the NYU study, researchers collected 78 responses from 52 players. In about half of comparisons with real Connections puzzles, participants rated the AI-generated versions as equally or more difficult, creative, and enjoyable. That result suggests the staged method can produce competitive examples; it does not establish that AI puzzles are consistently better, or that every generated puzzle will match the game’s editorial quality.
The difficulty ratings also point to a limitation: a model does not reliably predict how a human will experience a puzzle. Lead author Timothy Merino said, “Models like GPT don’t know how humans think, so they’re bad at estimating how tricky a puzzle is for the human brain.” A puzzle can satisfy instructions while still landing at the wrong difficulty for players.
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Could the method work for Codenames?
Possibly. NYU Game Innovation Lab director Julian Togelius identified Codenames as a potential application for a similar workflow, saying, “We could probably use a very similar method with good results.” This is a proposed transfer, not evidence that the study validated a Codenames system. The general idea—generate candidate material, have another model review it, and let a person select the best result—could be adapted, but each game would need evaluation against its own rules and player experience.
Quick Recap
Best Value
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