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Vector Database is a special database built to store and search embeddings fast. Regular databases search by exact matches; vector databases find similar meanings. Essential for RAG and semantic search.
A special database built to store and search embeddings fast. Regular databases search by exact matches; vector databases find similar meanings. Essential for RAG and semantic search.
Pinecone, Weaviate, and Supabase pgvector are popular choices for storing document embeddings.
The AI brain behind ChatGPT and similar tools. It's a massive program trained on tons of text that can understand and generate human-like writing. Think of it as autocomplete on steroids.
A technique that lets AI search your documents before answering questions. Instead of just making stuff up, it pulls real info from your data first. This is how you build a chatbot that actually knows your business.
A way to turn words, sentences, or documents into numbers that capture their meaning. Similar concepts get similar numbers, which lets AI find related content even if the exact words don't match.
Teaching an existing AI model new tricks by training it on your specific data. It's like hiring someone with general skills, then training them on how your company does things.
The art of writing instructions that get AI to do what you actually want. It's surprisingly important—the same AI can give garbage or gold depending on how you ask.