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Insights on AI communication, voice agents, WhatsApp automation, and the future of customer engagement.
5 min readUsing Synthetic Data to Train and Fine-Tune LLMs in 2026
Real training data is expensive, scarce, and legally complicated. Synthetic data offers an alternative. In 2026, it is mainstream for pre-training, fine-tuning, and benchmarking. When Synthetic Data Works 1. Data augmentation: Increase training set size in niche domains. 2. Privacy-sensitive domains…
Tutorial: Fine-Tune Llama 4 Scout for Your Domain
Llama 4 Scout — Meta's 17B-active-parameter MoE released in April 2025 with a 10M token context window — is one of the most capable open models available for domain fine-tuning in 2026. This tutorial walks through a LoRA fine-tune of Llama 4 Scout for a domain task, covering dataset prep, training, …
Fine-Tuning vs RAG: The 2026 Decision Framework
"Should we fine-tune or do RAG?" is a question that has lost most of its drama. By 2026 the field has settled on a clear answer: they do different things, and most production systems use both. The interesting question is no longer "which one?" but "what belongs in which?" The single most useful ment…
LoRA and Distillation: A Practical Guide for 2026
In 2026, a single consumer GPU is enough to specialize a 7B model on your domain in an afternoon. That is not a research milestone — it is the default. The two techniques that made it possible are LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA), with distillation as the cousin that compresses …