Generative AI Hub: Foundation Models, RAG Architecture, and Agent Workflows

Generative Artificial Intelligence represents a transformative paradigm shift in computational linguistics, multimodal reasoning, and software automation. Built upon deep Transformer neural networks, self-attention mechanisms, and massive-scale pre-training, generative foundation models generate human-grade natural language, synthesize code, create photorealistic imagery, and execute multi-step reasoning workflows.

This hub organizes foundation model architectures, retrieval systems, agentic cognitive loops, and production deployment infrastructures.

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1. Quick-Reference: Generative AI Architecture Layers

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|                               GENERATIVE AI ARCHITECTURE STACK                          |
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| Layer                  | Technologies                      | Key Technical Focus        |
+------------------------+-----------------------------------+----------------------------+
| Agent Orchestration    | LangGraph, Autogen, MCP Protocols | Planning, tool calling     |
| Retrieval & Context    | RAG, Hybrid BM25/Vector, KG RAG   | Grounding, Reciprocal Rank |
| Inference Acceleration | vLLM, TensorRT-LLM, Prompt Caching| PagedAttention, KV reuse   |
| Model Alignment        | RLHF, DPO, Parameter-Efficient LoRA| Steering, safety, domains  |
| Vector Indexing        | HNSW Graphs, IVF-PQ, ScaNN        | Sub-millisecond ANN search |
+-----------------------------------------------------------------------------------------+

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2. Core Deep Dives & Hubs

• AgenticAiHub: Autonomous agent cognitive architectures, tool use, and verification loops.
• PromptCaching: Key-Value attention cache reuse, token economics, and TTFT latency reduction.
• VectorIndexingInternals: HNSW graphs, Product Quantization, and Inverted File index internals.
• HNSW: Hierarchical Navigable Small World algorithms and vector similarity search.
• FineTuningLargeLanguageModels: LoRA, QLoRA, and supervised instruction tuning.