This demo walks through a hands-on AI effort where big language models and classic ML methods tried guessing Amazon product costs using heaps of customer reviews. Starting off with a 5GB batch of 46,726 home gadgets priced between $0 and $100, the scope later grew to cover car parts, gadgets, office supplies, hardware, phones, and toys – pulling in titles, descriptions, specs, and extra details. Instead of stacking models together, each one was tested solo: linear regression, SVR, and random forest – fed with custom features, bag-of-words, plus word2vec encodings. Results swung from a shaky $139 off using plain regression down to just $46 error – alongside an RMSLE of 0.38 – thanks to sharp-tuned, slimmed-down LLaMA 8B setups and other cutting-edge designs. All training and checks were tracked live on wandb.ai across 400K samples, revealing that deeper training runs plus tight tuning push accuracy way up.