Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding
arXiv:2607.05936v1 Announce Type: cross Abstract: Integration of web APIs is a cornerstone of modern software systems, yet writing correct web API invocation code remains challenging due to complex and evolving API specifications. Although LLMs are increasingly used for code generation, previous work has empirically shown that their ability to generate correct web API integrations is limited. At the same time, mitigation techniques and their effectiveness for this setting remain insufficiently understood. In this paper, we propose and systematically evaluate retrieval-augmented generation (RAG
The paper addresses a critical, current limitation in LLM code generation for web API integrations, a bottleneck for broader AI agent deployment.
Improving LLM reliability in API interactions is crucial for the development of robust AI agents capable of autonomous workflows and real-world system integration.
This research outlines methods to significantly enhance the accuracy and reliability of LLM-generated API calls, moving closer to dependable automation.
- · AI SaaS providers
- · Software developers
- · Enterprises adopting AI agents
- · LLM developers
- · Manual API integration specialists
- · Companies slow to adopt AI automation
More reliable and widespread adoption of LLMs for code generation and API integration.
Acceleration in the deployment and utility of AI agents across various industries, automating complex digital tasks.
Reduced development costs and faster innovation cycles as AI agents handle more sophisticated programming challenges.
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Read at arXiv cs.LG