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Build a private, domain-specific ChatGPT
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Module 1
Foundations of Text Representation and Chunking
1
Text Embeddings with Transformers
Explain how transformer-based embedding models convert text into dense vectors.
2
Comparing Embedding Models: Performance, Cost, and Suitability
Compare popular embedding models (e.g., Sentence Transformers, OpenAI) on dimensions like performance, cost, and domain suitability.
Evaluating Embedding Models: Benchmarks & Retrieval
Evaluate embedding models using both general-purpose benchmarks and task-specific retrieval performance.
4
Text Chunking Strategies: Impact on Context and Retrieval
Explain how different text chunking strategies (e.g., fixed-size, recursive, semantic) impact context preservation and retrieval granularity.
5
Choosing the Right Embeddings & Chunking: A Cost-Benefit Approach
Select an appropriate embedding model and initial chunking theory for the project based on a cost-benefit analysis.
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