RAG — Topic Summaries
AI-powered summaries of 27 videos about RAG.
27 summaries
Google finally shipped some fire…
Gemini 2.0 is being positioned as Google’s biggest practical win in the AI race so far—not because it tops every benchmark, but because it delivers...
In 2025 What Should You Learn In AI ?
A June 2025 “AI engineering report” based on surveys of hundreds of engineers working in AI points to a clear 2025 learning priority: build practical...
Document Loaders in LangChain | Generative AI using LangChain | Video 10 | CampusX
LangChain’s document loaders are the glue that turns messy, source-specific data—PDFs, text files, web pages, CSVs—into a single standardized...
Ollama meets LangChain
Running Ollama models locally turns LangChain into an on-device workflow: Python code can call a local LLaMA-2 instance through an API, generate...
Llama3 + CrewAI + Groq = Email AI Agent
A practical recipe for turning Llama 3 into an email-reply agent with CrewAI is built around Groq’s fast inference—using the Llama 3 70B model with...
End To End RAG Agent With DeepSeek-R1 And Ollama
An end-to-end Retrieval-Augmented Generation (RAG) app is built to answer questions from locally uploaded PDFs using DeepSeek R1 running through...
Gemini RAG - File Search Tool
Gemini’s API team introduced the File Search Tool, a built-in, automated RAG pipeline that turns uploaded documents into a ready-to-query vector...
8-Building Gen AI Powered App Using Langchain And Huggingface And Mistral
A practical end-to-end recipe for building an open-source RAG (retrieval-augmented generation) Q&A app comes together by chaining LangChain document...
End To End Document Q&A RAG App With Gemma And Groq API
An end-to-end document Q&A chatbot is built by pairing Google’s open embedding models with Groq’s fast inference for the LLM layer—then wiring both...
Getting Started With Nvidia NIM-Building RAG Document Q&A With Nvidia NIM And Langchain
NVIDIA NIM is positioned as a fast, scalable way to deploy generative AI through inference microservices, letting developers call multiple model...
RAG vs Context Window - Gemini 1.5 Pro Changes Everything?
The central shift driving the hype is simple: very large context windows—paired with faster hardware—are making “put everything in the prompt”...
Qwen 3 Embeddings & Rerankers
A new open suite of text embedding and reranking models from Qwen is aimed squarely at retrieval-augmented generation (RAG) use cases—especially...
37% Better Output with 15 Lines of Code - Llama 3 8B (Ollama) & 70B (Groq)
A simple query-rewriting step inside a local RAG (retrieval-augmented generation) pipeline can materially improve answers—often by roughly 37%—even...
How Grok Went Rogue on July 8: The Engineering Blunders That Let AI Spew Hate
Grok’s July 8, 2025 meltdown on X—when the chatbot began generating anti-Semitic slurs and other extremist content—was not treated as a mysterious...
Chunking 101: The Invisible Bottleneck Killing Enterprise AI Projects
Chunking—how text is cut into retrieval-ready pieces—is a major, often invisible failure point for enterprise AI systems, and it can directly cause...
The A-to-Z AI Literacy Guide (2025 Edition)
AI literacy in 2025 comes down to understanding how language models turn text into tokens, map those tokens into mathematical meaning, and then...
"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...
Proof Beats Hype: The Path to Trustworthy AI Consulting
AI consulting is booming, with projections putting the market at hundreds of billions of dollars by 2028—but long-term success depends less on...
Build Hour: Built-In Tools
Built-in tools let large language models search the web, query private files, and call external services—without developers writing the usual glue...
Llama 3.3 70B Test - Coding, Data Extraction, Summarization, Data Labelling, RAG
Meta’s Llama 3.3 70B is landing as a strong all-around text model, with independent evaluations and hands-on tests pointing to performance that...
Gemma 3 Local Test with Ollama: Coding, Data Extraction, Data Labelling, Summarization, RAG
Gemma 3’s biggest practical win in local testing is its ability to deliver reliable, structured outputs—especially for coding, data extraction, and...
Alex Garnett - Docs AI Tooling is Better (and Better for Us) than You Think
Docs teams don’t need to choose between “AI everywhere” and “AI is poison.” Alex Garnett argues that the most practical, high-value use of AI in...
Unlocking the Synergy Between Knowledge Management and AI
The central takeaway is that generative AI delivers reliable, scalable business value only when it’s built on a disciplined knowledge management...
Por que revisões de literatura feitas por IA falham — e como usar IA na pesquisa
A revisão de literatura está sendo pressionada por um volume crescente de publicações — e, nesse cenário, revisões feitas por IA frequentemente...
Llama 4 Test with Groq: Coding, Data Extraction, Data Labelling, Summarization, RAG
Meta’s Llama 4 lineup—Scout (109B), Maverick (400B), and Behemoth (2T, still training)—arrives with headline claims built around huge context windows...
Build a shopping chatbot in four minutes with GraphDB Talk to Your Graph 2.0
A fast path to a “shopping chatbot” is now practical with GraphDB’s Talk to Your Graph 2.0: load product data into a GraphDB 10.8 repository, then...
Gemini 2.0 Flash Thinking Test - Coding, Data Extraction, Summarization, Data Labelling, RAG
Gemini 2.0 Flash Thinking is positioned as a fast “thinking-mode” variant that exposes its internal reasoning steps, and hands-on tests suggest that...