Leveraging Generative AI in Search Infrastructure: Building Inference Pipelines for Enhanced Search Results

Abstract

Abstract

With the growing capabilities of generative AI, enhancing search infrastructures by building inference pipelines has become essential for achieving more relevant and context-aware search results. Traditional search engines are largely dependent on keyword matching and limited natural language processing techniques, which often fail to understand complex user intents or handle ambiguous queries effectively. Generative AI, particularly large language models (LLMs) and transformer-based architectures, enables deeper semantic understanding and the ability to generate contextually rich responses. By embedding generative AI into search pipelines, it becomes possible to deliver personalized and nuanced results, increasing both relevance and user satisfaction.

Inference pipelines equipped with generative AI can dynamically adapt to user queries, offering a multi-step process where search engines first analyze the query's intent and then employ the language model to retrieve and rank relevant information. This multi-layered approach involves stages such as query expansion, semantic matching, content summarization, and reranking of results, all driven by AI inferences. Advanced natural language understanding (NLU) models are used to decompose complex queries and match them against large datasets, while natural language generation (NLG) models summarize or rephrase responses for clarity. Moreover, generative AI can improve the search experience by providing contextual suggestions, summaries, or even direct answers to queries, thereby reducing user effort.

In practice, these inference pipelines can be integrated into existing search frameworks through microservices or APIs, allowing for modular scalability and ease of deployment across varied infrastructures. This setup supports real-time processing, low latency, and optimized resource allocation, essential for handling high query volumes. Additionally, with the advent of hybrid retrieval-augmentation systems, these AI-driven pipelines enable both keyword and semantic search capabilities, leading to a more robust, adaptable search experience.