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Gen AI RAG Implementation on AWS
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Hello Reader, Gen AI is here to stay! One of the most popular pattern in Gen AI is RAG, which stands for Retrieval Augmented Generation. In my day to day work as an AWS SA, this is coming up more and more in my customer conversations. In this newsletter edition, we will first level set what is RAG, and then show one of the implementation architecture on AWS. RAG (Retrieval Augmented Generation): 1. RAG (Retrieval Augmented Generation) is used where the response can be made better by using company specific context that the LLM does NOT have. You store relevant company data into a vector database. This is done by a process called embeddings where data is transformed into numeric vectors 2. User gives a prompt which can be made better by adding company specific info 3. A process (code/jupyter notebook/application) converts the prompt into vector and then search the vector database. Relevant info from the vector database is RETRIEVED (First Part of RAG) and returned 4. The original prompt is AUGMENTED (Second part of RAG) with this company specific info and sent to LLM 5. LLM GENERATES (Last part of RAG) the response and sends back to the user AWS Sample Implementation: The above is a Serverless implementation (Pay as you go, and no servers to manage!) for the RAG. Question for you readers - How are you studying Gen AI? Keep Learning and Keep Rocking 🚀, Cloud With Raj |
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Free Cloud Interview Guide to crush your next interview. Plus, real-world answers for cloud interviews, and system design from a top Solutions Architect at AWS.
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