Here are detailed Mermaid diagrams for each type of Augmented Generation, showing how they are implemented differently.
Retrieval-Augmented Generation (RAG)
Implementation: Retrieves relevant documents from external sources using vector search (e.g., FAISS) or keyword-based retrieval, then augments the LLM with context before generation.
Knowledge-Augmented Generation (KAG)
Implementation: Uses structured knowledge sources (e.g., Wikidata, domain ontologies) to retrieve factual information and integrate it into LLM responses.
Context-Augmented Generation (CAG)
Implementation: Uses conversation history, document history, or ongoing chat context to enhance response coherence and personalization.
Database-Augmented Generation (DAG)
Implementation: Queries structured databases (SQL, NoSQL) in real-time, retrieves relevant data, and integrates it into LLM-generated responses.
Prompt-Augmented Generation (PAG)
Implementation: Uses structured and optimized prompts (e.g., few-shot learning, chain-of-thought reasoning) to improve response accuracy.
Multimodal-Augmented Generation (MMAG)
Implementation: Uses multimodal processing (images, audio, video) to extract relevant information before passing it to the language model.
Fine-Tuned Augmented Generation (FAG)
Implementation: Uses a fine-tuned version of an LLM trained on specific datasets for domain specialization.
Symbolic-Augmented Generation (SAG)
Implementation: Integrates symbolic logic (e.g., rule-based systems, decision trees) to enhance reasoning.
Tool-Augmented Generation (TAG)
Implementation: Uses external APIs, calculators, or plugins to improve accuracy.
10. Agent-Augmented Generation (AAG)
Implementation: Uses multiple autonomous AI agents for complex problem-solving.
11. Graph-Augmented Generation (GAG)
Implementation: Uses graph databases (e.g., Neo4j) to retrieve relationships between entities.
12. RAG-2 (Next-Gen RAG)
Implementation: Enhances RAG with graph databases and structured data retrieval.
13. Learning-Augmented Generation (LAG)
Implementation: Uses continuous learning to refine responses over time.
Summary
Each Augmented Generation method enhances an LLM in a unique way:
RAG / RAG-2 → External retrieval sources
KAG → Knowledge graphs
CAG → Conversation context
DAG → Database queries
PAG → Optimized prompts
MMAG → Multimodal input
FAG → Fine-tuned models
SAG → Symbolic logic
TAG → External tools
AAG → Multi-agent collaboration
GAG → Graph databases
LAG → Adaptive learning