Implement Smarter LLMs Without Breaking the Bank: A Practical Guide to Fine-Tuning and LoRA
If you’re exploring AI integration or already working with LLMs, you’ve probably hit the same wall many teams do: pretrained models don’t always cut it out of the box.
This whitepaper breaks down two of the most effective solutions – Low-Rank Adaptation (LoRA) and full fine-tuning – to help you decide where to invest your time and resources.
Based on real implementation experience, this guide is built to help teams like yours unlock smarter, faster, and more scalable LLMs – without burning through budget or bandwidth.
Who this whitepaper is for
AI startups and product builders
Machine learning engineers and data teams
CTOs evaluating build-vs-buy decisions
Any team looking to deploy smarter, custom AI at scale
About the author
About the author
Mladen Lazic, COO of Scopic, joined the company in 2011 as a Developer and quickly rose to become respectively a Technical Lead and Director of Engineering. His exceptional contributions led to his promotion to Vice President of Engineering and Operations in 2020. As of 2022, he was promoted to COO.
In his current role, Mladen heads the overall business operations of Scopic, works with the CEO, Tim Burr, and the Management Team to implement the company’s strategic vision and values. He is also responsible for planning, implementing, managing, and overseeing multiple departments, including engineering, from a higher level. In 2023, he joined the Board of Directors, solidifying his position as a key member of Scopic’s leadership team.