Embeddings = turning words/sentences into numbers (vectors). These numbers represent meaning, not just letters.
Analogy: Imagine each word is a city → embedding = GPS coordinates on a map → words with similar meaning are close together.
Project: Build a semantic search (e.g., search your notes by meaning instead of exact keywords).
Most developers think of HTTPS as a checkbox. Something you enable because every tutorial tells you to.
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What is Prompt Engineering? Art of writing instructions for LLMs. Bad prompt → bad answer.
Hello Readers! My name is Amina, and I’m here to talk about my RAG. Combining search + LLM = RAG.
Vector DB = a special database to store embeddings (numbers). Let's you quickly find “closest meaning” results.
Training = feeding data to a model so it learns patterns. Example: Show a model 1M cat & dog pictures → it learns to…
Inference = using a trained model to get answers. Training = teaching the model.