The author of the article tested the capabilities of large language models (LLMs) in generating pipeline code for the Pipedrive API, specifically focusing on feature extraction, pipeline code generation, and memory-based intuition. The tests revealed that relying solely on LLMs' memory and intuition is unrealistic and that documentation quality and structure significantly impact the accuracy of feature extraction. The author also developed a structured extraction prompt to evaluate the feasibility of generating pipelines from APIs and identified key issues with Pipedrive's API documentation, including authentication and response formats. To overcome these limitations, the author suggests using partially-built pipelines and inspecting responses to gather missing information. The article concludes that establishing a definitive benchmark for AI-generated data pipelines is necessary to improve their accuracy and reliability.