Types of RAG (Generative AI)
1: Foundations Naive RAG, Simple RAG (Original), Simple RAG with Memory Foundations
2: Optimization Advanced RAG (Hybrid+Reranking), Modular RAG, HyDE Pipeline
3: Self-Reflective Self-RAG (Critique Tokens), Corrective RAG (CRAG), Adaptive RAG Evaluation
4: Multi-Path & Entities Agentic RAG, Graph RAG (Knowledge Graphs), Branched RAG, Speculative RAG Complex Reasoning
5: Multimodal Vision-Language Models, Visual Document Embedding (ColPali/CLIP) Cross-Modal
6: Matrix & Comparison Comprehensive 14-Architecture Comparison Table (Cost, Latency, Use Cases) Cheat Sheet
7: Production Template End-to-End Advanced RAG Pipeline with ChromaDB & FlashRank Reranker Full Implementation
1: Foundational RAG Architectures
1.1 Naive RAG
Architectural Concept: Single-step dense vector retrieval and direct prompt generation without filtering, reranking, or query translation.
Implementation Workflow: Document Ingestion → Text Chunking (e.g., 500 tokens) → Embedding Generation → Cosine Similarity Search → Top-k Prompt Injection → LLM Generation.
Pros & Cons:
Pros: Extremely fast response time, minimal computational overhead, simple to build and understand.
Cons: No relevance verification, vulnerable to irrelevant context injection and hallucinations on ambiguous queries.
Algorithm & Pseudo-Code — Naive RAG
- Input: Query Q, Vector Database V, LLM
- Output: Answer A
