Hi everyone,
There’s a massive trend right now towards "Infinite Context". The marketing pitch is: "Just dump your entire knowledge base into the prompt, the model will figure it out."
I think this is a dangerous trap.
From my experiments, even SOTA models suffer from attention dilution when the "Signal-to-Noise" ratio drops. If you feed a model 100k tokens, but 30k of those are semantic duplicates, boilerplate, or low-entropy garbage, the reasoning quality degrades (and you pay a fortune).
The Hypothesis: I believe we should focus less on "how much can we fit" and more on "how dense is the information."
To test this, I built an open-source project called EntropyGuard. It’s a local engine that attempts to quantify the "Information Density" of a dataset using Shannon Entropy and Semantic Similarity (Embeddings). It aggressively strips out data that doesn't add new bits of information to the context.
The Result: Cleaning a dataset by entropy/semantic dedup often reduces size by 40-60% while improving retrieval accuracy in RAG systems. It seems "dumber" models with cleaner data often beat "smarter" models with noisy data.
I’m looking for community perspective on the next step: I want to evolve this tool to solve the biggest "Data Hygiene" bottlenecks. If you work with AI, what is the missing link in your data prep?
- Semantic Chunking: Should we split text based on meaning shifts rather than character counts?
- Visual Audit: Do we need better UIs to "see" the noise before we delete it?
- Source Filtering: Is the problem actually in the ingestion (PDF parsing) rather than the cleaning?
I’d love to hear your thoughts on the Data-Centric AI approach vs. the Model-Centric approach. Are we lazy for relying on massive context windows?
Project link for those interested in the code:https://github.com/DamianSiuta/entropyguard
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