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英文字典中文字典相关资料:


  • REIC: RAG-Enhanced Intent Classification at Scale - arXiv. org
    REIC leverages retrieval-augmented generation (RAG) to dynamically incorporate relevant knowledge, enabling precise classification without the need for frequent re-training
  • Improve LLM responses in RAG use cases by interacting with the user
    This leads to unhelpful responses like “I don’t know” or incorrect, made-up answers provided by an LLM In this post, we demonstrate a solution to improve the quality of answers in such use cases over traditional RAG systems by introducing an interactive clarification component using LangChain
  • CUSTOM HYBRID GENAI-RAG FOR INTENT CLASSIFICATION
    Hybrid RAG revolutionizes intent classification by leveraging vector stores and LLMs to provide nuanced, accurate, and adaptive solutions, overcoming the limitations of traditional methods in real-world environments
  • RAG techniques: Cleaning user questions with an LLM
    Building AI apps using RAG (Retrieval Augmented Generation)? Consider using this technique of pre-processing user questions with an additional call to an LLM
  • Mastering RAG Chatbots: Semantic Router — User Intents
    Understanding intent is key to providing relevant responses in a RAG application A semantic router analyzes queries semantically to classify intent Based on this, it controls which
  • Intent Creation Extraction Using Large Language Models
    In this article I consider creating and using intents in the context of Large Language Models (LLMs) In an earlier article I reasoned that, as with AI in general, NLU Models also demand a
  • Develop a RAG Solution - Large Language Model End-to-End Evaluation . . .
    In this phase, you evaluate your Retrieval-Augmented Generation (RAG) solution by examining the expected user prompts that contain the retrieved grounding data against the language model Before you reach this phase, you should complete the preceding phases
  • RAG with User Interaction | Nikita Kozodoi
    This leads to unhelpful responses like “I don’t know” or incorrect, made-up answers provided by an LLM In this post, we demonstrate a solution to improve the quality of answers in such use cases over traditional RAG systems by introducing an interactive clarification component using LangChain
  • Enhancing LLM Accuracy with Mindful-RAG Framework
    Identify the Intent: Utilizing the model's intrinsic parametric knowledge, Mindful-RAG discerns the intent behind the question, focusing on keywords and phrases that clarify the depth and scope of the intent Identify the Context: The model analyzes the question's context, essential for formulating an accurate response
  • How Intent Classification Made a Financial AI Assistant Safe
    We think of this interweaving of intent classification and an LLM alongside a retrieval-augmented generation (RAG) system as “partial intent classification ” This practice — and the story behind it — shows how important it is for AI professionals to think creatively and continually experiment toward new solutions





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