Key Points

References

Key Concepts

Many models for RAG usage - Linkedin

By early 2025, over 51% of enterprise GenAI deployments use RAG architectures — up from 31% just a year earlier. And for good reason: it’s powering everything from customer support and legal automation to search and content generation. BUT real-world complexity demands modular, dynamic, and intelligent system architectures — not simplistic pipelines. What started as a simple retrieval pipeline (Naive RAG) is now evolving into the architectural backbone of large-scale, production-grade reasoning systems (Kudos to Weaviate for the graphic).

𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻:⬇️

𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚
➜ Retrieve documents, pass them to the LLM, generate an output.
- Fast to build
- Fragile when faced with ambiguity, long context, or conflicting information

𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲-𝗮𝗻𝗱-𝗥𝗲𝗿𝗮𝗻𝗸 𝗥𝗔𝗚
➜ Adds reranking to prioritize the most relevant information before generation.
- Improves accuracy and grounding
- Reduces risk of hallucinations

𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗥𝗔𝗚
➜ Extends retrieval and reasoning to include text, images, video, and audio.
- Critical for industries handling unstructured, diverse data types
- Unlocks new applications in healthcare, legal, automotive, and manufacturing

𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚
➜ Incorporates graph databases for structured reasoning across entities and relationships.
- Enables explainable AI
- Essential for compliance, auditing, supply chain, and knowledge management

𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗔𝗚
➜ Blends vector search, keyword search, and graph retrieval strategies.
- Maximizes robustness and adaptability across use cases
- Balances precision and recall for production environments

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 (𝗥𝗼𝘂𝘁𝗲𝗿)
➜ Uses agent-based orchestration to dynamically route queries to specialized tools, indexes, or retrieval strategies.
- Intelligent query handling
- Core enabler for autonomous workflows

𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗥𝗔𝗚
➜ Multiple agents collaborate, reason, retrieve, and act across distributed systems.
- Supports complex planning, tool use, and decision-making
- The foundation for enterprise-grade AI orchestration and multi-modal workflows

𝗥𝗔𝗚 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 — 𝗶𝘁’𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗚𝗲𝗻𝗔𝗜. 𝗘𝗮𝗰𝗵 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘀𝘁𝘆𝗹𝗲 𝘀𝗲𝗿𝘃𝗲𝘀 𝗮 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝗽𝘂𝗿𝗽𝗼𝘀𝗲 — 𝗳𝗿𝗼𝗺 𝘀𝗶𝗺𝗽𝗹𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘁𝗼 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀.

𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

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