Posts about foundations. ← All posts
Side-by-side comparison: ask the LLM a domain question with no context, then ask with retrieved context. The without-RAG answer is plausible nonsense. The with-RAG answer is correct. The example that motivates everything else in the course.
Hand-crafting vectors stops scaling at about 10 dimensions. LLM-generated embeddings give you a 1024-dim vector that captures semantic meaning. The example shows how to generate them and what they're good for.
The foundation. Build vectors by hand for a few words, compute cosine similarity, see why "cat" and "dog" come out closer than "cat" and "car." Demystifies everything that comes after.