Showing posts with label RAG vs CAG vs KAG. Show all posts
Showing posts with label RAG vs CAG vs KAG. Show all posts

Tuesday, 16 September 2025

The Data Engines Driving RAG, CAG, and KAG

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AI augmentation doesn’t work without the right databases and data infrastructure. Each approach (RAG, CAG, KAG) relies on different types of databases to make information accessible, reliable, and actionable.

RAG – Retrieval-Augmented Generation

Databases commonly used

  • Pinecone – Vector Database | Cloud SaaS | Proprietary license
  • Weaviate – Vector Database | v1.26+ | Apache 2.0 License
  • Milvus – Vector Database | v2.4+ | Apache 2.0 License
  • FAISS (Meta AI) – Vector Store Library | v1.8+ | MIT License

How it works:

  • Stores text, documents, or embeddings in a vector database.
  • AI retrieves the most relevant chunks during a query.

Real-World Examples & Applications

  • Perplexity AI → Uses retrieval pipelines over web-scale data.
  • ChatGPT Enterprise with RAG → Connects company knowledge bases like Confluence, Slack, Google Drive.
  • Thomson Reuters Legal → Uses RAG pipelines to deliver compliance-ready legal insights.

CAG – Context-Augmented Generation

Databases commonly used

  • PostgreSQL / MySQL – Relational DBs for session history | Open Source (Postgres: PostgreSQL License, MySQL: GPLv2 with exceptions)
  • Redis – In-Memory DB for context caching | v7.2+ | BSD 3-Clause License
  • MongoDB Atlas – Document DB for user/session data | Server-Side Public License (SSPL)
  • ChromaDB – Contextual vector store | v0.5+ | Apache 2.0 License

How it works:

  • Stores user session history, preferences, and metadata.
  • AI retrieves this contextual data before generating a response.

Real-World Examples & Applications

  • Notion AI → Reads project databases (PostgreSQL + Redis caching).
  • Duolingo Max → Uses MongoDB-like stores for learner history to adapt lessons.
  • GitHub Copilot → Context layer powered by user repo data + embeddings.
  • Customer Support AI Agents → Redis + MongoDB for multi-session conversations.

KAG – Knowledge-Augmented Generation

Databases commonly used

  • Neo4j – Graph Database | v5.x | GPLv3 / Commercial License
  • TigerGraph – Enterprise Graph DB | Proprietary
  • ArangoDB – Multi-Model DB (Graph + Doc) | v3.11+ | Apache 2.0 License
  • Amazon Neptune – Managed Graph DB | AWS Proprietary
  • Wikidata / RDF Triple Stores (Blazegraph, Virtuoso) – Knowledge graph databases | Open Data License

How it works:

  • Uses knowledge graphs (nodes + edges) to store structured relationships.
  • AI queries these graphs to provide factual, reasoning-based answers.

Real-World Examples & Applications

  • Google’s Bard → Uses Google’s Knowledge Graph (billions of triples).
  • Siemens Digital Twins → Neo4j knowledge graph powering industrial asset reasoning.
  • AstraZeneca Drug Discovery → Neo4j + custom biomedical KGs for linking genes, proteins, and molecules.
  • JP Morgan Risk Engine → Uses proprietary graph DB for compliance reporting.

Summary Table

Approach Database Types Providers / Examples License Real-World Use
RAG Vector DBs Pinecone (Proprietary), Weaviate (Apache 2.0), Milvus (Apache 2.0), FAISS (MIT) Mixed Perplexity AI, ChatGPT Enterprise, Thomson Reuters
CAG Relational / In-Memory / NoSQL PostgreSQL (Open), MySQL (GPLv2), Redis (BSD), MongoDB Atlas (SSPL), ChromaDB (Apache 2.0) Mixed Notion AI, Duolingo Max, GitHub Copilot
KAG Graph / Knowledge DBs Neo4j (GPLv3/Commercial), TigerGraph (Proprietary), ArangoDB (Apache 2.0), Amazon Neptune (AWS), Wikidata (Open) Mixed Google Bard, Siemens Digital Twin, AstraZeneca, JP Morgan


Bibliography

  • Pinecone. (2024). Pinecone Vector Database Documentation. Pinecone Systems. Retrieved from https://www.pinecone.io
  • Weaviate. (2024). Weaviate: Open-source vector database. Weaviate Docs. Retrieved from https://weaviate.io
  • Milvus. (2024). Milvus: Vector Database for AI. Zilliz. Retrieved from https://milvus.io
  • Johnson, J., Douze, M., & Jégou, H. (2019). Billion-scale similarity search with GPUs. FAISS. Meta AI Research. Retrieved from https://faiss.ai
  • PostgreSQL Global Development Group. (2024). PostgreSQL 16 Documentation. Retrieved from https://www.postgresql.org
  • Redis Inc. (2024). Redis: In-memory data store. Redis Documentation. Retrieved from https://redis.io
  • MongoDB Inc. (2024). MongoDB Atlas Documentation. Retrieved from https://www.mongodb.com
  • Neo4j Inc. (2024). Neo4j Graph Database Platform. Neo4j Documentation. Retrieved from https://neo4j.com
  • Amazon Web Services. (2024). Amazon Neptune Documentation. AWS. Retrieved from https://aws.amazon.com/neptune
  • Wikimedia Foundation. (2024). Wikidata: A Free Knowledge Base. Retrieved from https://www.wikidata.org

Monday, 15 September 2025

RAG vs CAG vs KAG: The Future of Smarter AI

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Artificial Intelligence is evolving at a breathtaking pace. But let’s be honest on its own, even the smartest AI sometimes gets things wrong. It may sound confident but still miss the mark, or give you outdated information.

That’s why researchers have been working on ways to “augment” AI to make it not just smarter, but more reliable, more personal, and more accurate. Three exciting approaches are leading this movement:

  • RAG (Retrieval-Augmented Generation)
  • CAG (Context-Augmented Generation)
  • KAG (Knowledge-Augmented Generation)

Think of them as three different superpowers that can be added to AI. Each solves a different problem, and together they’re transforming how we interact with technology.

Let’s dive into each step by step.

1. RAG – Retrieval-Augmented Generation

Imagine having a friend who doesn’t just answer from memory, but also quickly Googles the latest facts before speaking. That’s RAG in a nutshell.

RAG connects AI models to external sources of knowledge like the web, research papers, or company databases. Instead of relying only on what the AI “learned” during training, it retrieves the latest, most relevant documents, then generates a response using that information.

Example:
You ask, “What are Stellantis’ electric vehicle plans for 2025?”
A RAG-powered AI doesn’t guess—it scans the latest news, press releases, and reports, then gives you an answer that’s fresh and reliable.

Where it’s used today:

  • Perplexity AI – an AI-powered search engine that finds documents, then explains them in plain English.
  • ChatGPT with browsing – fetching real-time web data to keep answers up-to-date.
  • Legal assistants – pulling the latest compliance and case law before giving lawyers a draft report.
  • Healthcare trials (UK NHS) – doctors use RAG bots to check patient data against current research.

👉 Best for: chatbots, customer support, research assistants—anywhere freshness and accuracy matter.

2. CAG – Context-Augmented Generation

Now imagine a friend who remembers all your past conversations. They know your habits, your preferences, and even where you left off yesterday. That’s what CAG does.

CAG enriches AI with context i.e. your previous chats, your project details, your personal data, so it can respond in a way that feels tailored just for you.

Example:
You ask, “What’s the next step in my project?”
A CAG-powered AI recalls your earlier project details, your goals, and even the timeline you set. Instead of a generic response, it gives you your next step, personalized to your journey.

Where it’s used today:

  • Notion AI – drafts project updates by reading your workspace context.
  • GitHub Copilot – suggests code that fits your current project, not just random snippets.
  • Duolingo Max – adapts lessons to your mistakes, helping you master weak areas.
  • Customer support agents – remembering your last conversation so you don’t have to repeat yourself.

👉 Best for: personal AI assistants, adaptive learning tools, productivity copilots where personalization creates real value.

3. KAG – Knowledge-Augmented Generation

Finally, imagine a friend who doesn’t just Google or remember your past but has access to a giant encyclopedia of well-structured knowledge. They can reason over it, connect the dots, and give answers that are both precise and deeply factual. That’s KAG.

KAG connects AI with structured knowledge bases or graphs—think Wikidata, enterprise databases, or biomedical ontologies. It ensures that AI responses are not just fluent, but grounded in facts.

Example:
You ask, “List all Stellantis electric cars, grouped by battery type.”
A KAG-powered AI doesn’t just summarize articles—it queries a structured database, organizes the info, and delivers a neat, factual answer.

Where it’s used today:

  • Siemens & GE – running digital twins of machines, where KAG ensures accurate maintenance schedules.
  • AstraZeneca – using knowledge graphs to discover new drug molecules.
  • Google Bard – powered by Google’s Knowledge Graph to keep facts accurate.
  • JP Morgan – generating compliance reports by reasoning over structured financial data.

👉 Best for: enterprise search, compliance, analytics, and high-stakes domains like healthcare and finance.

Quick Comparison

Approach How It Works Superpower Best Uses
RAG Retrieves external unstructured documents Fresh, real-time knowledge Chatbots, research, FAQs
CAG Adds user/session-specific context Personalized, adaptive Assistants, tutors, copilots
KAG Links to structured knowledge bases Accurate, reasoning-rich Enterprises, compliance, analytics

Why This Matters

These aren’t just abstract concepts. They’re already shaping products we use every day.

  • RAG keeps our AI up-to-date.
  • CAG makes it personal and human-like.
  • KAG makes it trustworthy and fact-driven.

Together, they point to a future where AI isn’t just a clever talker, but a true partner helping us learn, build, and make better decisions.

The next time you use an AI assistant, remember: behind the scenes, it might be retrieving fresh data (RAG), remembering your context (CAG), or grounding itself in knowledge graphs (KAG).

Each is powerful on its own, but together they are building the foundation for trustworthy, reliable, and human-centered AI.


Bibliography

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