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Natural Language Processing (NLP)

Sascha Block2025-01-24T15:57:08+01:00Tags: AllenNLP, API integration, Artificial intelligence, Attention mechanism, Azure Text Analytics, BERT, Bias reduction, bidirectional models, Chatbot development, Computational linguistics, computational semantics, Constituent parsing, Context-aware models, Contextual embeddings, Conversational AI, Corpus analysis, Data preprocessing, Deep learning, Dependency parsing, Dialog systems, ethical AI, Fairness in AI, Gated Recurrent Unit (GRU), GloVe, Google Cloud NLP, GPT, Handling sensitive data, Hugging Face, IBM Watson, Information retrieval, Language comprehension, Language models, Lemmatization, Long Short-Term Memory (LSTM), machine learning, morphological analysis, Multilingual NLP, Named Entity Recognition (NER), natural language processing, neural networks, NLP, NLTK, NLU (Natural Language Understanding), OpenNLP, Parsing algorithms, Privacy, PyTorch, Question answer, recurrent neural networks (RNN), Self-Attention, semantic analysis, Sentiment analysis, Sentiment classification, Sequence modeling, spaCy, Speech recognition, Speech-to-text, Stemming, syntactic analysis, TensorFlow, Text analysis, Text classification, Text generation, Text mining, Text parsing, Text summary, Text-to-speech, Tokenization, Transformer models, virtual assistants, Word embeddings, Word type tagging, Word2Vec|

Natural Language Processing (NLP) - a highly specialized subfield of artificial intelligence (AI) How do machines understand human language? Natural Language Processing is the key to bridging the gap between humans and machines

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Retrieval Augmented Generation (RAG) – using AI models effectively

Sascha Block2024-03-30T10:36:37+01:00Tags: Advanced RAG, AI language models, AI technology, Artificial intelligence, Data processing, Dialog systems, FAQ, Language models, Modular RAG, multimodal applications, Naive RAG, Question and answer systems, RAG Systems|

What is Retrieval Augmented Generation (RAG)? Retrieval-Augmented Generation (RAG) is an advanced AI technique in AI language modeling based on the integration of external information sources to improve and augment answer generation. A RAG system combines the comprehensive knowledge capacities of a Large Language Model (LLM) with the ability to obtain specific information from an external knowledge repository. This AI method allows the model to generate answers based not only on its internally trained knowledge, but also on current, specific and extensive external data. Find out more now!

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