RAG

🔹 Traditional RAG (Retrieval-Augmented Generation)

• Definition: A pipeline where a Large Language Model (LLM) is combined with an external knowledge base (usually vector DB + embeddings).

• Flow:

  1. User Query → Converted into an embedding.

  2. Retriever → Finds the most relevant documents/chunks from the knowledge base.

  3. Augmentation → Retrieved docs are appended to the user query.

  4. LLM → Generates the final answer using the context.

• Purpose:

  • To overcome LLM’s knowledge cutoff.

  • To give grounded, fact-based responses (not hallucinations).

• Limitations:

  • Only retrieves text-based documents.

  • Doesn’t learn or adapt — every query is stateless.

  • Context window limitations (too much retrieved text can overwhelm the LLM).

🔹 GenAI RAG (Next-Gen or Advanced RAG)

• Definition: An evolved RAG approach where GenAI + multiple enhancements are added to improve retrieval and reasoning.

• Enhancements over Traditional RAG:

  1. Multi-modal retrieval: Can fetch not just text, but also structured data (SQL, APIs, graphs, PDFs, images, audio).

  2. Agentic RAG: Uses AI agents to decide how and where to retrieve (e.g., from API, DB, knowledge graph, or vector DB).

  3. Re-ranking: Adds intelligent ranking (not just cosine similarity) — often uses cross-encoders or fine-tuned models for better relevance.

  4. Context compression: Summarizes long documents before passing them to the LLM (avoids token waste).

  5. Memory-augmented: Keeps past interactions (conversational memory), so queries aren’t stateless.

  6. Dynamic enrichment: Can trigger external tools, perform reasoning, or chain-of-thought before answering.

• Purpose:

  • More accurate, domain-aware answers.

  • Better at handling complex enterprise scenarios (like BFSI, healthcare, legal).

  • Enables multi-agent collaboration (e.g., one agent retrieves from SQL, another from docs, another validates).

✅ In short:

• Traditional RAG = "Search + Stuff" → Retrieve docs → Give to LLM → Get answer.

• GenAI RAG = "Intelligent Retrieval + Reasoning" → Adds multi-modal retrieval, agentic orchestration, re-ranking, context compression, memory, and tool usage for much smarter answers.