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+LangGraph is a library for building stateful, multi-step workflows using LangChain agents and tools. It introduces a graph-based abstraction where nodes represent steps (e.g., retrieval, generation, decision-making) and edges define transitions between them.
+
+Key Concepts:
+- Nodes: Functional units like retrievers, LLMs, or tool wrappers.
+- Edges: Conditional or static paths that control flow between nodes.
+- State: A shared dictionary passed and updated across nodes.
+
+LangGraph supports asynchronous execution, retries, and streaming, making it ideal for complex agentic workflows.
+
+Retrieval-Augmented Generation (RAG) is a technique that enhances LLM responses by injecting external knowledge. It typically involves:
+1. Querying a vector store to retrieve relevant documents.
+2. Passing those documents as context to the LLM.
+3. Generating a grounded, context-aware response.
+
+Benefits of RAG:
+- Reduces hallucinations by anchoring responses in real data.
+- Enables domain-specific QA without fine-tuning.
+- Improves transparency and traceability.
+
+LangChain provides the building blocks for RAG:
+- Document loaders and chunkers
+- Embedding models and vector stores (e.g., FAISS, Chroma)
+- Chains and agents for orchestration
+
+LangGraph extends LangChain by enabling:
+- Explicit control flow
+- Modular design
+- Debuggable, reproducible workflows
+
+Use Cases:
+- Academic QA bots
+- Internal knowledge assistants
+- Modular agent systems with tool use
+
+This primer serves as a foundational reference for building explainable, modular GenAI systems using LangGraph and RAG.