RAG (Retrieval-Augmented Generation) is an [[🔎 Artificial Intelligence (AI)|AI]] architecture that combines large language models with external knowledge retrieval systems to generate more accurate, up-to-date, and contextually relevant responses. RAG represents a fundamental shift in how one can leverage AI - moving from generic responses to domain-specific intelligence. This technology enables you to build AI systems that understand their unique context, data, and requirements. RAG helps AI give you answers that are: - **More accurate** (based on real documents, not just memory) - **Up-to-date** (pulls from current information) - **Specific to your situation** (searches your own documents and data) - **Verifiable** (can show you where the information came from) Easy example: Let's say you ask "What's our company's vacation policy?" - **Without RAG**: The AI might give you a generic answer about typical vacation policies - **With RAG**: The AI searches your company's HR documents, finds your actual policy, and gives you the specific rules that apply to your workplace **Direct Ways to Use RAG Today** Pre-Built Solutions: - **[[💾 ChatGPT|ChatGPT]] Enterprise/Teams**: Upload company documents for AI to reference in responses - **[[🏦 Microsoft ($MSFT)|Microsoft]] Copilot**: Integrates with SharePoint, OneDrive, and Office documents automatically - **[[🏦 Alphabet ($GOOGL, $GOOG)|Google]] Bard/Gemini**: Can search and reference specific Google Drive files - **Notion AI**: References your Notion workspace content when answering questions - **Slack GPT**: Searches your Slack history and shared documents Build Your Own RAG System: - **LangChain + OpenAI**: Popular framework for custom RAG applications - **Pinecone + GPT**: Vector database plus language model integration - **Chroma + Local [[🔎 Large Language Models (LLMs)|LLMs]]**: Open-source option for sensitive data - **AWS Bedrock**: Managed RAG service with enterprise security **When RAG is Essential vs Optional** RAG is Critical When: - Agents need access to proprietary company data - Working with frequently updated information (legal, financial, medical) - Agents must provide verifiable, source-attributable responses - Operating in regulated industries requiring audit trails RAG Less Critical When: - Agents perform purely computational tasks (data analysis, calculations) - Working with static, well-known domains - Creative or generative tasks where accuracy matters less than novelty - Simple automation workflows with predefined logic **RAG's Role in Agentic Workflows** Agentic Workflows Defined: AI systems that can plan, execute multiple steps, use tools, and make decisions [[🔎 Autonomous AI agent|autonomously]] to complete complex tasks. - **Knowledge Grounding**: Agents need accurate, current information to make good decisions - **Tool Integration**: RAG often serves as the "memory" tool that agents query - **Context Persistence**: Agents reference previous conversations, documents, and outcomes - **Domain Expertise**: Agents operating in specialized fields require domain-specific knowledge **RAG Workflow Pattern**: ``` User Query → Search/Retrieve Relevant Info → Combine Query + Retrieved Info → Generate Response ``` **Non-RAG Workflow Pattern**: ``` User Query → Generate Response (using only model training data) ```