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Full Guide on LLM Synthetic Data Generation

Unite.AI

However, the real power of LLM-driven synthetic data generation lies in more sophisticated techniques and applications. Advanced Techniques for Synthetic Data Generation 2.1 Prompt Engineering Prompt engineering is crucial for guiding LLMs to generate high-quality, relevant synthetic data.

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Open Artificial Knowledge (OAK) Dataset: A Large-Scale Resource for AI Research Derived from Wikipedia’s Main Categories

Marktechpost

However, acquiring such datasets presents significant challenges, including data scarcity, privacy concerns, and high data collection and annotation costs. Artificial (synthetic) data has emerged as a promising solution to these challenges, offering a way to generate data that mimics real-world patterns and characteristics.

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Addressing the Challenges in Multilingual Prompt Engineering

Heartbeat

Multilingual prompt engineering is the art and science of creating clear and precise instructions for AI models that understand and respond in multiple languages. This article discusses the difficulties that multilingual prompt engineering encounters and solutions to those difficulties.

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Best practices to build generative AI applications on AWS

AWS Machine Learning Blog

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. When applying these approaches, we discuss key considerations around potential hallucination, integration with enterprise data, output quality, and cost.

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Achieving accurate image segmentation with limited data: strategies and techniques

deepsense.ai

Prompt engineering : the provided prompt plays a crucial role, especially when dealing with compound nouns. By using “car lamp” as a prompt, we are very likely to detect cars instead of car lamps. The first concept is prompt engineering. Text: The model accepts text prompts. Source: own study.

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Unpacking the NLP Summit: The Promise and Challenges of Large Language Models

John Snow Labs

Strategy and Data: Non-top-performers highlight strategizing (24%), talent availability (21%), and data scarcity (18%) as their leading challenges. Currently, getting LLMs to function as agents requires a lot of messy prompt engineering, and the technology seems quite finicky and unready for use in production.”

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Achieving accurate image segmentation with limited data: strategies and techniques

deepsense.ai

Prompt engineering : the provided prompt plays a crucial role, especially when dealing with compound nouns. By using car lamp as a prompt, we are very likely to detect cars instead of car lamps. The first concept is prompt engineering. Text: The model accepts text prompts. Source: own study.