Research on Generative AI Assistants Based on Large Language Model APIs

Abstract

With the breakthrough of Large Language Models (LLMs) technology, API call based generative AI assistants have become the core tool for enterprise intelligence transformation. This article systematically studies the design and implementation of a generative AI assistant system that supports multimodal interaction, dynamic task routing, and privacy protection by calling mainstream LLM APIs such as OpenAI GPT-4, Anthropic Claude, and Google Gemini. Experiments have shown that the system achieves a task completion rate of 94% in complex tasks such as medical diagnosis and legal document generation, which is 38% higher than traditional rule engines, while reducing API call costs by 42%. This article deeply analyzes the problem of information decay in long context processing and proposes a dynamic summarization algorithm based on attention weight decay, which improves the accuracy of multi round dialogue memory to 89%. In addition, to address the risk of API dependency, a hybrid scheduling framework combining open-source models and commercial APIs is designed to reduce the probability of single point of failure while ensuring performance. The research results provide a full stack reference for the engineering implementation of LLM, from architecture design to optimization strategies.

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