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AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
I am leading GTM adventures in AI, Insurance and iBanking. Building new and marvelous cloud apps and systems to make customers, advisors and agents lives easier. AI ++
There are many patterns for RAG as one considers end to end work in real world use cases.
This is useful...
Expand it as you apply multiple levels of RAG.
#ai#rag
AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
Great visual breakdown of RAG variants! This infographic makes it so much easier to grasp the nuances of different approaches. 👏 #AI, #RAG, #MachineLearning
AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
🚀 The 7 Most-Used Retrieval-Augmented Generation (RAG) Architectures
Looking to implement RAG but unsure where to start? Here's a visual guide to the top RAG architectures being used today:
1️⃣ Naive RAG: A straightforward setup combining retrieval and generation.
2️⃣ Retrieve-and-Rerank: Enhances context relevance with reranking models.
3️⃣ Multimodal RAG: Works across multiple data types like text, images, and video.
4️⃣ Graph RAG: Utilizes graph databases for better context and relationships.
5️⃣ Hybrid RAG: Combines multiple retrieval techniques for optimal results.
6️⃣ Agentic RAG (Router): Routes queries to specialized retrieval agents.
7️⃣ Agentic RAG (Multi-Agent): Uses multiple tools, from vector databases to APIs like Slack or Gmail, for enriched responses.
💡 Whether you're working with documents, graphs, or multimodal data, these architectures offer a pathway to build state-of-the-art systems.
👉 Check out the infographic for a breakdown of each architecture and their components. Which one are you most excited to try? Let’s discuss!
AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
This is a great cheat sheet for navigating the evolving landscape of RAG architectures! The integration of graph-based reasoning and multimodal retrieval is definitely exciting
AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
RAG is the technique to get the right context information so that the LLM responses would be useful. It’s a fun world on its own with different architecture approaches and solutions…
AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
Driving AI Transformation with Multi-Cloud, Automation and Data Platforms | Global Practice Leader |Cloud| Automation | Data | AI | Industry speaker| community builder
AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 popular RAG architectures by Weaviate:
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share
AI Software Engineer | ML & GenAI | MLOps | Google Dev Student Club
🚀 Struggling to keep up with new RAG variants?
Here’s a cheat sheet of 7 of the most popular RAG architectures by Weaviate
✅ RAG architectures represent the intersection of retrieval-based systems and generative AI, delivering contextually rich and accurate results.
✅ Each architecture caters to specific scenarios, ensuring scalability and domain-specific adaptability.
✅ Incorporating graph-based reasoning, multimodal retrieval, and agentic capabilities ensures these systems stay relevant in the evolving AI landscape.
What’s your favorite RAG architecture? Let’s discuss in the comments! 😀
#RAG#LLM#AI#ML#AgenticAI#VectorDB#Share