Basic to Advanced: Retreival-Augmented Generation (RAG)

Learn how retrieval augmented generation merges language models with external data to deliver contextually relevant answers. Explore external knowledge integration, dynamic information, and contextual response generation powered by vector search.

Explore the principles of retrieval-augmented generation, grounding LLM outputs in factual information and enhancing knowledge on demand without retraining, with on-demand retrieval and transparent source logs.

Understand the retrieval-augmented generation architecture: a framework performs semantic search over a vector database, curates data, prompts an llm, and post-processes grounded responses using OpenAI and local llms.

Explore the differences between open source and closed source LMS, learn when to use each, and see how LMS fits into the RAC workflow alongside RAC and LM.

Discover the fundamentals of large language models, including transformer-based LLMs, GPT variants, learning types, few-shot learning, and open-source deployment with ulama and Hugging Face Transformers.

Explore closed and open source LLMs like Claude, Gemini, and Llama 3.1, comparing context windows, multimodal capabilities, and prompt caching to support RAG workflows and practical applications.

Compare open source and closed source models, highlighting accessibility, setup complexity, API usage, customization limits, cost, safety, and performance trade-offs for building retrieval-augmented generation applications.

Learn how retrieval-augmented generation grounds llms outputs with retrieved sources, boosting factual accuracy and reducing hallucinations. Explore dynamic querying and domain-specific retrieval, plus related techniques.

Install an open source model locally with ulama, on Mac OS, Linux, or Windows, and run a terminal-based setup to access llama, mistral, and gemma two via a local api.

Download Python from python.org, install Visual Studio Code, and set up Jupyter Notebook using brew install Jupyter or pip install notebook on Windows.

Access the complete rack based repository covering vector database, LMS, rack pipeline, and AWS EC2 deployment guides. Clone via Git, open in code, and work hands-on with notebooks and prompts.

Learn about vector databases, including indexing, vector search, retrievals, and embeddings, with hands-on coding in VS code as theory gives way to practical work.

Explore vector databases and embeddings in a retrieval-augmented generation pipeline, learn how to transform data into vectors, and perform fast similarity search with embedding models.

Explore embeddings as dense vector representations that capture semantic meaning, generated by GPT embeddings. Learn how vector databases store and query these embeddings for real-time similarity search in RAG.

Explore how word2vec, sentence transformer, ResNet 18, node2vec, and librosa generate and visualize word, sentence, image, graph, and audio embeddings in 2D/3D for multimodal retrieval.

Explore storing embeddings in a vector database using a file-based library from Facebook or Pinecone, compare annoy and NSW tradeoffs, and build a retrieval-augmented generation workflow with sentence transformers.

Learn to build a basic rag pipeline with LangChain components: loaders, embeddings, vector database, retriever, llm, prompt template, and orchestrator.

Build a basic retrieval augmented generation pipeline to power website, memory-based, and csv chatbots. Leverage OpenAI embeddings, chunked web content via BeautifulSoup, and a vector store with prompts.

Build a csv based chatbot by using a csv loader to load csv data, create embeddings and a vector store, and wire a prompt chain to answer questions like price.

Explore advanced topics in retrieval-augmented generation theory and use-case driven implementations, with a VS code example and techniques like query expansion and contextual filtering for your rack based workflow.

Master advanced retrieval augmented generation techniques, including multi-vector retrieval, knowledge-enhanced retrieval, hybrid and cross-modal search, contextual reranking, query expansion, and text splitting with context preservation and semantic-coherent chunking.

Explore query expansion in a retrieval-augmented generation workflow, using paraphrased queries to boost hits in vector databases with LangChain and OpenAI models.

Explore prompt caching in retrieval-augmented generation (RAG) by implementing in-memory and SQLite caches with LangChain, reducing latency and enabling cache invalidation and reinforcement feedback.

Rack-based chatbot that fetches website content, creates embeddings, and answers via a retrieval-augmented generation flow using Streamlit, LangChain, and the deep sea carbon model.

Explore the 2025 AI roadmap, from operator agents and browser-enabled AI to robotics, infinite memory, and enterprise workflow agents, plus open source models and inference optimization.

Master retrieval-augmented generation to build an advanced rack-based workflow and host it on the AWS ecosystem, leveraging GitHub repos and AI agents.