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🚀 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
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
Keeping up with the latest RAG (Retrieval-Augmented Generation) architectures can be challenging. The overview of variants from Naive RAG to Agentic RAG, highlighting how each approach enhances retrieval and generation processes.
Architectures like Hybrid RAG and Multi-Agent RAG offer promising solutions for handling more complex and dynamic tasks. Exploring these evolving techniques can provide valuable insights into the future of AI.
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
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
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
🛠️ Understanding RAG Architectures – A Technical Breakdown 🚀📊
Retrieval-Augmented Generation (RAG) architectures are revolutionizing how AI systems combine retrieval and generation to deliver context-rich, accurate results. These architectures vary in design, each tailored for specific use cases and technical requirements. Let’s break it down:
🔗 1. Scalability & Adaptability: Modern RAG systems rely on vector databases like Weaviate or Pinecone for high-throughput, low-latency retrieval. Advanced techniques such as hybrid search (dense + sparse embeddings) and query expansion optimize results for domain-specific challenges, enabling systems to scale without compromising accuracy. 🧠📊
🧩 2. Graph-Based Reasoning: Advanced RAG designs incorporate Graph Neural Networks (GNNs) to manage entity relationships and logical reasoning across datasets. This enables multi-hop reasoning, where agents dynamically connect distant knowledge nodes to infer meaningful insights—perfect for complex research or multi-step problem-solving tasks. 📚🔍
🌐 3. Multimodal Retrieval: RAG isn't limited to text anymore. Modern architectures integrate images, audio, and tabular data into shared vector spaces using cross-modal embeddings. This enables AI systems to address tasks like visual Q&A, OCR document parsing, or audio-driven search systems effectively. 🎤📷🔗
🤖 4. Agentic RAG Workflows: Agent-based RAG systems combine task planning, orchestration, and multi-agent collaboration to handle complex workflows. Frameworks like LangGraph allow for dynamic sub-task execution, ensuring reasoning processes remain consistent across multi-step workflows. These systems shine in strategy planning, market research, and iterative decision-making tasks. ⚙️🛡️
📊 5. Advanced Retrieval Techniques: Techniques like re-ranking algorithms (BM25, ColBERT), context chunking, and sliding windows improve how relevant data is retrieved and structured for LLMs. Combined with noise filtering, these optimizations ensure higher response accuracy and model focus during generation. 📝🔄
🔥 6. Choosing the Right RAG Architecture: The choice between Naive, Advanced, Agentic, or Multimodal RAG depends on factors like task complexity, latency constraints, data modality, and scalability needs. Staying informed about these architectures allows teams to make informed technical decisions and build robust, adaptable AI solutions. 🚀💡
🙏 Thanks for sharing this insightful overview! Discussions like these help demystify the nuances of RAG architectures and guide teams in building smarter, more efficient AI systems. Looking forward to more technical insights! 🚀📚
#RAG#AI#GenerativeAI#AgenticAI#MachineLearning#TechInnovation#VectorDB#KnowledgeRetrieval#MultimodalAI#AIArchitecture 🛠️🔍📚
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
🚀 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
Enterprise AI Adoption Expert | AI Solutions Builder | Serial Entrepreneur in AI | Innovator in AI Strategy and Transformation
2wExceptionnally valuable content Muhammad Zarar , thanks for sharing 💡