RAG Retrieves. Fine-Tuning Forgets. HyperNetworks Inject - and Now We Have the Scaling Laws.
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There are two mainstream answers to "how do I get my LLM to reliably use proprietary data?" RAG retrieves relevant chunks at inference time. Fine-tuning bakes new knowledge into the weights. Both are widely deployed. Both have well-documented failure modes. A new paper from Nace AI and Purdue University proposes a third path backed by the first systematic scaling laws: HyperNetwork-based knowledge injection . Instead of modifying the LLM, you train a second network that generates LoRA adapters…
1Key Takeaways
- There are two mainstream answers to "how do I get my LLM to reliably use proprietary data?" RAG retrieves relevant chunks at inference time.
- Fine-tuning bakes new knowledge into the weights.
- Both have well-documented failure modes.
- A new paper from Nace AI and Purdue University proposes a third path backed by the first systematic scaling laws: HyperNetwork-based knowledge injection .
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that there are two mainstream answers to "how do I get my LLM to reliably use proprietary data?" RAG retrieves relevant chunks at inference time.
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