From 295 Tips to 92: How We Built a Chess Consensus from 20 AIs
Here's a problem nobody warns you about when you ask 20 AI models the same question: they give you 295 answers, and at least a third of them are saying the same thing in different words.
When we built our Top Chess Tips ranking, this was the biggest technical challenge. Not getting the data — that part was straightforward. If working through 295 tips has you itching to actually play, we've linked to some top-rated chess boards on Amazon worth checking out. The hard part was figuring out that "Learn pins, forks, and skewers," "Master basic tactical motifs," "Study double attacks and discovered checks," and nine other entries were all essentially the same tip.
The Duplicate Problem
Each of our 20 AI models — GPT-5.4, Claude Opus 4.6, Grok 4.20, DeepSeek V3.2, Llama 4 Maverick, and 15 others — independently generated a ranked list of chess tips. No model saw any other model's answers. The raw output: 295 individual entries.
But when you look at those 295 entries, patterns emerge fast. Here's a real example from our data. These are all separate entries from different models:
- "Don't bring the queen out too early"
- "Avoid early queen development"
- "Keep the queen back in the opening"
- "Don't develop your queen prematurely"
- "The queen should not be deployed in the opening"
Five different models. Five different phrasings. One tip. If we counted each as separate, the ranking would be polluted with near-duplicates, and the consensus scoring wouldn't reflect reality. A tip that appeared in 9 models' lists would look like 5 different tips with 1-2 appearances each.
The Worst Offender: Tactical Motifs
The single worst duplication cluster was tactical motifs. Twelve separate entries all said some version of "learn your tactics." Here's a sample:
- "Understand pins, forks, and skewers"
- "Master basic tactical patterns"
- "Study double attacks"
- "Learn discovered checks and double checks"
- "Practice knight forks"
- "Recognize common tactical motifs"
After merging, this cluster became a single entry — "Understand Pins, Forks, and Skewers" — ranked #7 with 12 appearances and a 39% confidence score. The low confidence despite high appearances tells you something: models agreed tactics matter, but couldn't agree whether it's a top-3 tip or a mid-list one.
How the Deduplication Works
Simple string matching won't cut it. "Castle early for king safety" and "Get your king safe by castling quickly" share almost no words in common, but they're the same advice. We needed semantic understanding.
Our deduplication engine uses AI itself — specifically GPT-4o-mini — to compare entries and determine whether they're semantically equivalent. For each potential duplicate pair, the model returns a confidence score. Above 85%, we auto-merge. Between 60-85%, the pair goes to a human review queue. Below 60%, they stay separate.
The process runs automatically whenever a ranking has more than 20 entries, which means it kicks in for every project we publish. For the chess ranking specifically, it processed 295 entries and identified approximately 70 duplicates across 25 distinct topic clusters.
What the Clusters Reveal
The duplication patterns are interesting in their own right. Here are the largest clusters we found in the chess data:
| Topic Cluster | Entries Merged | Final Rank |
|---|---|---|
| Tactical motifs (pins, forks, skewers) | 12 | #7 |
| Don't bring the queen out early | 5 | #8 |
| Piece development | 4 | #3, #5 |
| Rook activation on open files | 3 | #6 |
| Pawn structure management | 3 | #9 |
The biggest insight: chess tips about tactics are the most linguistically diverse. Models have a dozen different ways to say "learn your forks." Tips about center control, by contrast, were remarkably consistent — nearly every model used the words "control" and "center" together, making them easy to identify.
Before and After
The numbers tell the story:
- Before dedup: 295 raw entries from 20 models
- Duplicates identified: ~70 entries across 25 topic clusters
- After dedup: 92 unique tips
- Reduction: 69% fewer entries, zero information lost
The key metric is that last one: zero information lost. Every merge combines the appearance counts and weighted scores from the duplicate entries into the surviving entry. When 5 models all said "don't bring the queen out early," the merged entry gets credit for all 5 appearances. The consensus score goes up. The ranking becomes more accurate, not less.
Why This Matters Beyond Chess
This deduplication challenge shows up in every consensus ranking we build — not just chess. When we ranked the best products in other categories, we found similar patterns. AI models express the same ideas in wildly different ways, and any consensus system that doesn't account for that will produce bloated, repetitive rankings.
The chess ranking was our most dramatic example because chess tips are abstract concepts, not named products. There's no SKU or brand name to match on — just natural language describing strategic principles. It's the hardest possible deduplication problem, and it forced us to build a system that actually understands meaning, not just matches strings.
The result is a cleaner, more honest ranking. 92 tips, each one unique, each one scored by how many independent AI models thought it was worth mentioning.
See the full ranking: Top Chess Tips and Tricks from 20 different Ai's. Learn more about how our scoring works.