Generative AI Architecture Explained (Part 1)
📰 Medium · LLM
Learn the basics of Generative AI architecture and how prompts are converted to vector embeddings
Action Steps
- Read about the GenAI pipeline on Medium to understand its components
- Build a simple prompt-to-vector-embedding model using popular libraries like Hugging Face Transformers
- Configure a vector database to store and query embeddings efficiently
- Test the model with various prompts to analyze its performance
- Apply the knowledge of vector embeddings to improve your own GenAI models
Who Needs to Know This
AI engineers and data scientists can benefit from understanding the GenAI pipeline to improve their models and workflows
Key Insight
💡 Vector embeddings are a crucial step in the GenAI pipeline, enabling efficient storage and querying of prompt representations
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🤖 Understand how prompts become vector embeddings in GenAI pipelines!
Key Takeaways
Learn the basics of Generative AI architecture and how prompts are converted to vector embeddings
Full Article
From Your Prompt to Vector Embeddings: Understanding the First Half of the GenAI Pipeline Continue reading on Medium »
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