Imagine having a search engine that understands the nuances of your queries, not just the keywords. Researchers are exploring just that with a new technique called Retrieval Augmented Retrieval with In-Context Examples (RARe). Traditional search engines rely on matching keywords in your query to keywords in documents. This often misses the mark, especially with complex or ambiguous searches. RARe enhances search relevance by providing the search engine with "context clues." These clues are similar queries paired with their ideal results. By learning from these examples, the search engine can better understand the intent behind your search and retrieve more relevant results. The results? RARe boosts search accuracy by up to 2.72% in normalized Discounted Cumulative Gain (nDCG), a key metric for search relevance. This improvement is particularly noticeable in searches involving reasoning and complex tasks. While promising, RARe faces a challenge: speed. Processing these context clues adds computational overhead, increasing search latency, especially for smaller datasets. However, as datasets grow larger, this overhead becomes less significant compared to the time spent actually searching the data. Future research will focus on optimizing this process and expanding RARe's capabilities to longer text formats and multiple languages. This research opens exciting avenues for developing a new generation of search engines that understand our needs better than ever before, offering a more intuitive and intelligent search experience.