Recent advances in large language models (LLMs) are revolutionizing recommender systems (RS). LLM-based RS typically relies on a large number of training samples (more than 10%) and long-range interaction sequences to capture more historical information, aiming to surpass the performance of traditional recommendation models. However, excessive historical data can introduce outdated or irrelevant noisy user behaviors, and the use of large amounts of historical data and oversized training sets can increase computational and storage overheads, thereby reducing model training efficiency. Therefore, it is essential to model user interaction sequences in a more streamlined and precise manner. To achieve this, we propose the Two-stage Augmented Retrieval and Alignment for Recommendation (TARARec) for RS, which aims at few-shot scenarios based on lightweight LLM finetuning. In the first stage, TARARec introduces a seed instruction set generated by an external LLM to multi-task joint fine-tuning, and the second stage employs a hybrid similarity manipulation module to seamlessly integrate versatile fine-grained similarities for RS training. To evaluate the performance of TARARec, we implement comprehensive experiments on three real-world datasets. The results shows considerable performance in zero or few-shot scenarios with short interaction sequences.