Vector Databases — Topic Summaries
AI-powered summaries of 13 videos about Vector Databases.
13 summaries
Vector databases are so hot right now. WTF are they?
Vector databases are surging because they turn raw text, images, and audio into searchable “meaning” using embeddings—and then use that similarity...
Complete RAG Crash Course With Langchain In 2 Hours
Retrieval-Augmented Generation (RAG) is presented as a practical way to make large language models answer with up-to-date, domain-specific...
What Is Agentic RAG?
Agentic RAG upgrades traditional retrieval-augmented generation by adding an intelligent routing layer that decides which knowledge base to consult...
Most Popular Framework-Langchain vs LangGraph
LangChain and LangGraph both help build LLM-powered applications, but they’re optimized for different kinds of workflows: LangChain is built around a...
Prompt Engineering Vs RAG Vs Finetuning Explained Easily
The clearest way to choose between prompt engineering, RAG, and fine-tuning is to match the technique to where the needed knowledge should come from:...
Exploring Job Market Of Generative AI Engineers- Must Skillset Required By Companies
Generative AI engineering jobs are converging on a clear, repeatable skill stack: strong software development plus hands-on experience building and...
Freelancing, Consultant And Remote Jobs Are Increasing For Generative AI
Generative AI demand is translating into real freelancing and consulting opportunities—especially for people who can build end-to-end applications...
What Is LLM HAllucination And How to Reduce It?
LLM hallucination is what happens when a large language model produces confident answers that are not factually correct—often by “making up” details...
The AI Ops Engineer - Next BIG Role in Tech? 🤖
A new “AI Ops Engineer”–style role is taking shape around turning rapidly evolving foundation models into working, shipped products—without requiring...
"Training" an AI Agent for ONE Specific TASK with OpenAI-o1 API
A hands-on experiment builds a highly constrained Reddit “commenting” agent around OpenAI o1, using retrieval-augmented generation (RAG) plus strict...
Build Better RAGs with Contextual Retrieval
Contextual retrieval boosts retrieval-augmented generation (RAG) accuracy by enriching every text chunk with extra, chunk-specific context derived...
Masterclass: Knowledge Graphs & Massive Language Models — The Future of AI, RelationalAI | KGC 2023
Conversational AI is being treated as a new “computer for humans,” but the practical breakthrough isn’t that it behaves like people—it’s that it can...
How RAG Finds Answers in Millions of Documents | Embeddings, Vector Databases, LangChain & Supabase
Retrieval in RAG hinges on one practical step: turning a user question into a vector and then finding the most semantically similar document chunks...