https://genai.gitpull.in/4-embedding-and-vectors/index.html What Are Word Embeddings? Word embeddings are numerical representations of words in a continuous vector space. These embeddings capture the meaning, relationships, and context of words based on how they appear in text data. Why Do LLMs Need Word Embeddings? LLMs like GPT, BERT, and LLaMA work with numbers, not raw text. Embeddings convert words into numerical format so they can be processed by neural networks. Without embeddings: The model treats words like independent tokens (e.g., “king” and “queen” would be unrelated). With embeddings: The model understands relationships (e.g., “king” and “queen” are semantically close). Key Idea: Words with similar meanings will have similar vector representations in the embedding space. Hugo en-us https://genai.gitpull.in/4-embedding-and-vectors/4.1-custom-embeddings/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://genai.gitpull.in/4-embedding-and-vectors/4.1-custom-embeddings/index.html Custom Embeddings: Why, When, and How? Why Would You Need Custom Embeddings? Pre-trained embeddings (like OpenAI’s text-embedding-ada-002 or SentenceTransformers) work well in most cases. However, custom embeddings are necessary when: Domain-Specific Knowledge If you’re working with medical, legal, finance, or technical text, general-purpose embeddings may not capture key relationships. Example: “BP” in general NLP models means “British Petroleum,” but in medicine, it means “Blood Pressure.” Multilingual Support Many embedding models are optimized for English, so custom training is needed for non-English or code-mixed languages (e.g., Hinglish). Fine-Tuned Retrieval & Search https://genai.gitpull.in/4-embedding-and-vectors/4.2-custom-embeddings-examples/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://genai.gitpull.in/4-embedding-and-vectors/4.2-custom-embeddings-examples/index.html