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I spent 40+ hours researching and creating these 10 viral Business Strategy Cheat Sheets that yielded 2,166,816 total impressions since the middle of December 2023.   And today, I’m giving them all away for free.   Why? Because business leaders around the globe desperately need to improve their business acumen for career growth and upward mobility.   Seriously, I aim to give back as much as I can.   Here’s how YOU can help give back as well!   Simply do this: 1. Like this post 👍 2. Repost it ♻️ So your network can see it. 3. Comment "Send" 📋   And I'll send you the link via message. Only available for the first 24 hours! ------ P.S. Repost this to be the hero of your network ♻️   Here’s what you get:   1/ The Decision Matrix Cheat Sheet 1.0 – Impressions – 1,035,610 2/ The Decision Matrix Cheat Sheet 2.0 – Impressions – 348,999 3/ The Decision Matrix Cheat Sheet 3.0 – Impressions – 47,518 4/ The Decision Matrix Cheat Sheet 4.0 – Impressions – 130,742 5/ The Blue Ocean Strategy Cheat Sheet 1.0 – Impressions – 235,164 6/ The Blue Ocean Strategy Cheat Sheet 2.0 – Impressions - 103,940 7/ The Ultimate Business Strategy Matrix – Impressions - 117,446 8/ The Ultimate Productivity Matrix - Impressions – 49,498 9/ The Ultimate Cognitive Bias Cheat Sheet – Impressions – 43,231 10/ The Ultimate Negotiation Strategy Cheat Sheet – Impressions – 54,688 #fuelyourgrowth #linkedin #business

To successfully use 𝗥𝗔𝗚 in your 𝗟𝗟𝗠 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀, your 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕 must constantly be updated with the latest data. Here is how you can implement a 𝘀𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 to keep your vector DB in sync with your datasets ↓ . 𝗥𝗔𝗚 is a popular strategy when building LLMs to add context to your prompt about your private datasets. Leveraging your domain data using RAG provides 2 significant benefits: - you don't need to fine-tune your model as often (or at all) - avoid hallucinations . On the 𝗯𝗼𝘁 𝘀𝗶𝗱𝗲, to implement RAG, you have to: 3. Embed the user's question using an embedding model (e.g., BERT). Use the embedding to query your vector DB and find the most similar vectors using a distance function (e.g., cos similarity). 4. Get the top N closest vectors and their metadata. 5. Attach the extracted top N vectors metadata + the chat history to the input prompt. 6. Pass the prompt to the LLM. 7. Insert the user question + assistant answer to the chat history. . But the question is, 𝗵𝗼𝘄 do you 𝗸𝗲𝗲𝗽 𝘆𝗼𝘂𝗿 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕 𝘂𝗽 𝘁𝗼 𝗱𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝗱𝗮𝘁𝗮? ↳ You need a real-time streaming pipeline. How do you implement it? You need 2 components: ↳ A streaming processing framework. For example, Bytewax is built in Rust for efficiency and exposes a Python interface for ease of use - you don't need Java to implement real-time pipelines anymore. 🔗 Bytewax: https://lnkd.in/dWJytkZ5 ↳ A vector DB. For example, Qdrant provides a rich set of features and a seamless experience. 🔗 Qdrant: https://lnkd.in/dtZuyiBp . Here is an example of how to implement a streaming pipeline for financial news ↓ #𝟭. Financial news data source (e.g., Alpaca): To populate your vector DB, you need a historical API (e.g., RESTful API) to add data to your vector DB in batch mode between a desired [start_date, end_date] range. You can tweak the number of workers to parallelize this step as much as possible. → You run this once in the beginning. You need the data exposed under a web socket to ingest news in real-time. So, you'll be able to listen to the news and ingest it in your vector DB as soon as they are available. → Listens 24/7 for financial news. #𝟮. Build the streaming pipeline using Bytewax: Implement 2 input connectors for the 2 different types of APIs: RESTful API & web socket. The rest of the steps can be shared between both connectors ↓ - Clean financial news documents. - Chunk the documents. - Embed the documents (e.g., using Bert). - Insert the embedded documents + their metadata to the vector DB (e.g., Qdrant). #𝟯-𝟳. When the users ask a financial question, you can leverage RAG with an up-to-date vector DB to search for the latest news in the industry. Bytewax and Qdrant make this easy 🔥 #machinelearning #mlops #deeplearning