This study evaluates the performance of seven large language models (LLMs) in generating context-aware recommendations. The system is built on a collection of PDF documents (brochures) describing local events and activities, which are embedded into an FAISS vector store to support semantic retrieval. Synthetic user profiles are defined to simulate diverse preferences, while static weather conditions are incorporated to enhance the contextual relevance of recommendations. To further improve output quality, a reranking step, utilizing Cohere’s API, is used to refine the top retrieved results before passing them to the LLMs for final response generation. This allows better semantic organization of relevant content in line with user context. The main aim of this research is to identify which models best integrate multimodal inputs, such as user intent, profile attributes, environmental context and how these insights can inform the development of adaptive, personalized recommendation systems. The main contribution of this study is a structured comparative analysis of 7 LLMs, applied to a tourism-specific RAG framework, providing practical insights into how effectively different models integrate contextual factors to produce personalized recommendations. The evaluation revealed notable differences in model performance, with Qwen and Phi emerging as the strongest performers, whereas LLaMA frequently produced irrelevant recommendations. Moreover, many models favored gastronomy-related venues over other types of attractions. These findings indicate that although the RAG framework provides a solid foundation, the selection of underlying models plays an important role in achieving high quality recommendations.

Recent advances in large language models (LLMs) highlight two dominant strategies for performance improvement: prompt engineering and fine-tuning. While prompt design can significantly influence model output, it remains uncertain whether lightweight fine-tuning methods, such as adapter-based training, offer meaningful advantages for structured, domain-specific tasks. This study builds on prior research comparing three prompting strategies for natural-language command parsing into JSON schemas. Expanding that framework, the current work investigates how adapter-based fine-tuning, where most model parameters are frozen and only small adapter modules are trained, affects model accuracy and consistency. The experiment uses the same controlled shopping-cart parsing task and dataset of 12,000 synthetic commands to ensure direct comparability. Results quantify the trade-off between computational cost and performance gains, offering evidence-based insights into whether fine-tuning is a justified investment compared to advanced prompt engineering. The contribution of this study is a clear, empirical framework for deciding when fine-tuning meaningfully enhances LLM utility in applied natural-language understanding.

The integration of natural language understanding into conversational commerce requires robustly extracting structured intents from idiomatic text. While cloud-based models are highly capable, their latency, privacy risks, and operational costs necessitate efficient local alternatives. To address this, we empirically evaluate locally deployed, open-weights Large Language Models (LLMs) on parsing multi-intent shopping cart commands into deterministic JSON schemas. We benchmark multiple models across minimal, extended, and few-shot prompting paradigms, utilizing a dual-axis framework to simultaneously measure extraction quality (operation F1-score, exact match, schema validity) and hardware performance (latency, GPU power consumption). Our evaluation highlights a fundamental trade-off between schema compliance and computational overhead, demonstrating how advanced prompting techniques improve parsing accuracy while measurably impacting inference latency and power draw. Ultimately, this comprehensive profiling identifies optimal model configurations for resource-constrained environments: a well-chosen 3-8B parameter model (specifically Granite4 3B) paired with few-shot prompting recovers over 95% of the quality of the largest evaluated model at a coarse indicative estimate of 40× lower operational energy per command, establishing a practical baseline for deploying cost-effective LLMs as efficiency candidates in resource-constrained environments.