• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
• grounding — Tying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.
• alignment — The work of making AI systems actually pursue what their builders and users intend, rather than something subtly or dangerously different.

Names the failure modes before you start: a smaller filtered dataset often beats a larger raw one, and synthetic data can encode biases that leave the domain model less trustworthy than the base.

• articleWhy Methods Like Qlora Fall Short In Domain Knowledge Injection 2
• articleOptimizing Arcee Foundation Models On Intel Cpus
• articleResearch Spotlight 3 Learnings From 3 Use Cases Of Mergekit
• articleVirtuoso Lite Virtuoso Medium V2 Distilling Deepseek V3 Into 10b 32b Small Language Models Slms

• articleWhen Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey ResponsesZihan Chen, Di Zhu, Lei Nico Zheng
• articleCase Study Innovating Domain Adaptation Through Continual Pre Training And Model MergingArcee AI editorial sitemap
• clipFine-tuning improves alignment even for unseen groupsAI Engineer

Checking sign-in…

Loading comments…