Lamini AI has developed a new technique called Memory Tuning for large language models (LLMs) that significantly improves factual accuracy and reduces hallucinations. This innovation has achieved 95% accuracy, compared to the typical 50% seen with other approaches, and has reduced hallucinations from 50% to just 5%. The Memory Tuning method involves tuning millions of expert adapters with precise facts on top of any open-source LLM, embedding facts within the model to retrieve only the most relevant information during inference. This dramatically lowers latency and costs while maintaining high accuracy and speed. This approach is particularly crucial for applications requiring exact fact recall, such as converting natural language questions into SQL database queries, where accuracy is paramount. Traditional methods like Prompting and Retrieval-Augmented Generation (RAG) have their place in improving LLM accuracy but often fall short of eliminating hallucinations. Lamini Memory Tuning overcomes this by combining information retrieval techniques with AI. Implementing Lamini Memory Tuning promises higher accuracy, lower costs, and faster development cycles, enabling broader adoption and deployment in various industries. As Lamini AI continues to refine this technology, the potential for fully automated, highly accurate AI-driven solutions becomes increasingly attainable. For practical AI solutions, consider the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. To evolve your company with AI, identify automation opportunities, define KPIs, select an AI solution, and implement gradually. For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram or Twitter. Useful Links: - AI Lab in Telegram @itinai – free consultation - Twitter – @itinaicom
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