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AI generations
Unit 1
Langchain
Introduction to Langchain
Applications of Langchain in AI Generations
Langchain and Natural Language Processing
Langchain and Neural Networks
Unit 2
Retrieval Augmented Generations
Introduction to Retrieval Augmented Generations
Retrieval Techniques in AI Generation
Applications of Retrieval Augmented Generations
Unit 3
Vector Embeddings
Introduction to Vector Embeddings
Word Embeddings
Image Embeddings
Graph Embeddings
Unit 2 • Chapter 3
Applications of Retrieval Augmented Generations
Summary
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Concept Check
What is an application of Retrieval Augmented Generations (RAG)?
Speech synthesis
Natural language understanding
Image recognition
Sentence generation
How does Retrieval Augmented Generations (RAG) contribute to language models?
Speeds up transcription
Improves text summarization
Enhances long-form generation
Enhances image recognition
What is a key feature of Retrieval Augmented Generations (RAG) for text-to-text models?
Limited training data
Manual search options
Fixed data sources
Dynamic data retrieval
In what way does Retrieval Augmented Generations (RAG) enhance generated outputs?
Reduces word count
Adds grammatical errors
Adds random text snippets
Improves coherence and factuality
What distinguishes Retrieval Augmented Generations (RAG) from traditional language models?
Better punctuation usage
Limited vocabulary range
Higher word generation speed
Ability to retrieve and condition on external knowledge
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Retrieval Techniques in AI Generation