Content Moderation on Gen AI Apps, The World as a Figment of Our Imagination & Operations Management
Closer Consulting
A Data Science company specialized in Business Intelligence, Data Engineering and Artificial Intelligence.
Content Moderation on Gen AI Applications: Similarity Method
💻 The surge in demand for Generative AI (Gen AI) has led businesses to integrate these technologies to enhance user experiences and streamline operations. Given that many Gen AI applications directly interact with users, ensuring the quality and appropriateness of generated content is essential to protect users, uphold brand reputation, and comply with regulatory standards.
🔎 Two common methods for content moderation in Retrieval-Augmented Generation (RAG) applications are built-in LLM safeguards and prompt instructions. Built-in safeguards in LLMs like OpenAI and Llama restrict content related to violence, hate, or self-harm. However, businesses may need more specific restrictions, such as avoiding discussions about competitors. Prompt instructions can limit a chatbot’s responses, but LLMs are prone to hallucinations, potentially leading to unwanted outputs.
🗃️ The Similarity Method offers an additional layer of moderation. It verifies content twice: when received from the user and before delivering the final answer. This method involves creating a Content Moderation Index, a vector database of approved and blocked content, allowing for similarity checks. If a statement matches blocked content above a predefined score, it triggers an action to block it.
✅ The success of this method depends on defining an appropriate cut-off score to balance false positives and negatives. While not a standalone solution, the Similarity Method enhances content moderation by providing a simple, quick and effective supplementary layer to existing safeguards, ensuring safer and more reliable Gen AI interactions.
The World As a Figment Of Our Imagination
🛰 During the last INSIGHTS event, promoted by DSPA - Data Science Portuguese Association, João Pires da Cruz addressed the boundless potential that lies at the intersection of imagination and technology. In an era of limitless possibilities, it was displayed how to envision a new horizon for Data Science.
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Operations Management
💳 A smartcard system company was dealing with manual distribution of a high volume of activities among a large team of over 300 backoffice operators. It was a time-consuming and error-prone process, leading to inconsistent service levels, delayed response times and a significant strain on operational efficiency. Moreover, the lack of real-time data and insights hindered the company's ability to make informed decisions and identify areas for improvement.
🤖 To address these pain points, the company implemented Evalyze, an AI-powered tool that automates task distribution. By optimizing workload allocation, Evalyze boosted productivity by 30%, handling over 7,000 tasks daily.
📈 This resulted in improved customer service through faster response times and consistent quality, while providing real-time data for informed decision-making.
⚡ Quick Links
🔗 AI achieves silver-medal standard solving International Mathematical Olympiad problems | Google DeepMind
Wow, this edition of Data Wave is sure to make waves! 🌊 Thank you for sharing these cutting-edge insights!
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
2moIt's exciting to see #DataWave continue to highlight the intersection of data science and AI. On a deeper level, this means we're moving beyond descriptive analytics towards predictive and prescriptive models that truly empower organizations. Given your focus on "challenging complexity," what novel techniques are you exploring to address the ethical considerations arising from increasingly sophisticated AI systems?