Spade’s Post

Understanding RAG using Analogy. 🏅Retrieval Augmentated Generation (RAG) 🏅 Complex concepts are always easier to grasp with the right analogy. That’s exactly what happened to me. I met Kannan Sankaran who is an AI practitioner in Deutsche Bank. While discussing my learning and the assignment I am working on for the GEE training, I mentioned that I needed to use RAG but didn’t fully understand it. That’s when he explained what RAG is with the following analogy: 🚀Alex, a technology expert, joins a bank and is tasked with developing a new fintech application that leverages the bank’s existing data and systems to provide enhanced services to customers. Here’s how the RAG framework can be understood through this scenario: ➡️ Retrieval: Alex, the technology expert, needs to understand the bank’s existing data systems, customer needs, and regulatory requirements to develop the new fintech application. He consults Jamie, a seasoned data manager at the bank who has been with the organization for over a decade. Jamie retrieves detailed information about the bank’s data architecture, customer behavior patterns, and compliance guidelines from the bank’s extensive databases and records. ➡️Augmentation: Jamie doesn’t just provide all the information he has; instead, he curates the top 5 most relevant datasets and insights that Alex needs. This includes key data points on customer transaction behaviors, existing API documentation, regulatory compliance checklists, and any past attempts at similar projects. This ensures that Alex receives the most pertinent and actionable information. ➡️ Generation: Armed with these curated insights, Alex uses his technological expertise to design and develop the new fintech application. He integrates the retrieved data with his own knowledge of modern fintech solutions to create a user-friendly and compliant application that meets the bank’s and customers’ needs. 🔥 Alex is the LLM. 🔥 Jamie is the retrieval mechanism with domain specific information I have refined this analogy using #ChatGPT to make it more crisp and #DallE has generated appropriate picture. I think they have done nice job. What do you say? Thanks Kannan Sankaran for very nice explanation. I’d love to hear your thoughts, LinkedIn friends. Do you have any other analogies that might explain this better? What do you think? #RAGFramework #LLM #InformationRetrieval #KnowledgeAugmentation #ArtificialIntelligence

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📈 Imagine a world where financial services are seamless and more human-centric. #GenAI is changing financial services companies making them faster, more efficient and customer-friendly. By automating tasks like loan underwriting, AI is freeing up human talent do what it does best, innovate and solve complex problems. 🚀 #FutureReady #Fintech #AIRevolution https://accntu.re/3MmSdKh

📈 Imagine a world where financial services are seamless and more human-centric. #GenAI is changing financial services companies making them faster, more efficient and customer-friendly. By automating tasks like loan underwriting, AI is freeing up human talent do what it does best, innovate and solve complex problems. 🚀 #FutureReady #Fintech #AIRevolution https://accntu.re/3MmSdKh

📈 Imagine a world where financial services are seamless and more human-centric. #GenAI is changing financial services companies making them faster, more efficient and customer-friendly. By automating tasks like loan underwriting, AI is freeing up human talent do what it does best, innovate and solve complex problems. 🚀 #FutureReady #Fintech #AIRevolution https://accntu.re/3MmSdKh

Are you a fintech company? Are you working on an intelligent chatbot for obtaining financial insights? Don't use text2sql LLM for building queries to your database where you store your historical financial information. It may work correctly in 7 out of 10 and incorrectly in 3 cases. Use LLM only for bucketing requests via chatbot, and next invoke human-crafted query with 100% accuracy to your database. The solution based on text2sql with 100% accuracy is at least in order of magnitude more complex than the proposed one. #financialservices #chatbots #AI #datamanagement #llm #text2sql #fintech

📈 Imagine a world where financial services are seamless and more human-centric. #GenAI is changing financial services companies making them faster, more efficient and customer-friendly. By automating tasks like loan underwriting, AI is freeing up human talent do what it does best, innovate and solve complex problems. 🚀 #FutureReady #Fintech #AIRevolution https://accntu.re/3MmSdKh

📈 Imagine a world where financial services are seamless and more human-centric. #GenAI is changing financial services companies making them faster, more efficient and customer-friendly. By automating tasks like loan underwriting, AI is freeing up human talent do what it does best, innovate and solve complex problems. 🚀 #FutureReady #Fintech #AIRevolution https://accntu.re/3MmSdKh

📈 Imagine a world where financial services are seamless and more human-centric. #GenAI is changing financial services companies making them faster, more efficient and customer-friendly. By automating tasks like loan underwriting, AI is freeing up human talent do what it does best, innovate and solve complex problems. 🚀 #FutureReady #Fintech #AIRevolution https://accntu.re/3MmSdKh

📈 Imagine a world where financial services are seamless and more human-centric. #GenAI is changing financial services companies making them faster, more efficient and customer-friendly. By automating tasks like loan underwriting, AI is freeing up human talent do what it does best, innovate and solve complex problems. 🚀 #FutureReady #Fintech #AIRevolution https://accntu.re/3MmSdKh