AI Builders Global Conference 2026. October 14-16, 2026. Online.
Fine-Tuning LLMs: Strategies, LoRA & Domain Adaptation
This description belongs to the 2026 topic catalog. The 2027 program is being prepared separately.
When do you fine-tune vs. use RAG? This workshop provides tactical guidance on the decision framework and implementation: LoRA and QLoRA techniques, domain-specific adaptation for legal, medical, and coding tasks, data curation and quality for fine-tuning datasets, evaluation benchmarks, and detailed cost analysis comparing fine-tuned models vs. prompted large models.
Questions explored in the 2026 topic catalog
- When should you fine-tune vs. use RAG vs. prompt engineering?
- What are the practical differences between LoRA, QLoRA, and full fine-tuning?
- How do you curate high-quality datasets for domain-specific fine-tuning?
- What evaluation benchmarks measure fine-tuned model quality?
- How does the cost of fine-tuned models compare to API-based inference?
- What are the best open-source base models for fine-tuning in 2026?
Who this topic is for
ML engineers, data scientists, and AI developers who need to build specialized models for specific domains or use cases.
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