Large Language Models (LLMs) are powerful tools for code generation, yet they often struggle with complex and specialized tasks, leading to hallucinations and suboptimal code output. This issue arises particularly when dealing with intricate libraries or detailed application demos, where conventional fine-tuning methods fall short.
Join our exclusive Fullstack Live-Event and explore a groundbreaking technique designed to overcome these challenges. We will dive into real-world experiences with fine-tuning code generators on advanced generative AI libraries like Langchain and Vertex AI. Learn about the importance of meticulous data representation and how it plays a crucial role in enhancing LLM performance.
Exploring or already using AI tools like Langchain and Vertex AI for code-related tasks.
Interested in fine-tuning code generators to produce high-quality, precise outputs.
Facing challenges with AI-generated code hallucinations and seeking practical strategies to mitigate them.
Looking to enhance your understanding of data representation in the context of AI-driven code generation.
Working on complex and specialized coding tasks that require advanced AI techniques.
Shomron Jacob is the Head of Applied Machine Learning & Platform at Iterate.ai. Shomron began his career as a software engineer but soon found himself learning ML/AI and, a few years ago, switched his professional direction to follow it. He lives in Silicon Valley.
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