Objective, Inc.’s Post

Does your search engine know whether "bat" should bring up results for sports gear, animals, or a fictional character? We've just launched Finetuning into self-service Private Beta, so now it can! 🎉 With Finetuning, you can teach your search engine to understand the unique context of your business. Some of our partners are already seeing a 25% improvement in relevance by training their search indexes. All it takes is a single API call. 📈 So excited to deliver these innovations to our customers with the folks at Objective, Inc. Much more to come 🚀

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RAG vs Fine-Tuning... which do you use? Daliana Liu outlines the nuances of RAG vs Fine-Tuning in language models—fine-tuning for style precision or RAG for data traceability. Learn more about the comparison on model customization, interpretability, and latency in this guide. Which approach suits your data science needs? #datascience #largelanguagemodels #techtalks

RAG vs Fine-Tuning (3 highlights): • Model Customization: RAG < Finetuning Finetuning allows adjustments of LLM behavior, writing style, or specific domain knowledge based on specific tones or terms. • Interpretability: RAG > Finetuning: RAG's responses can be traced back to specific data sources while finetuning is similar to a black box, it is not always clear why the model reacts a certain way. • Latency: RAG > Finetuning: RAG involves data retrieval, so the latency is higher. These are just a few comparisons and the table is a great cheatsheet to help you decide when to use what (source below). If you want to learn more about building & evaluating RAG Apps, here is a beginner-friendly workshop on Feb 21, register here: https://lnkd.in/eDnrvhMj RAG vs Finetuning, which one do you use? Share this with a friend who might find it useful. *Table Source: Paper - "Retrieval-Augmented Generation for Large Language Models: A Survey" (https://lnkd.in/eUPYjCZX)

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Why do vegetable bags have green stripes Full Article Link >>> https://lnkd.in/g74rpj8D close 01/8What does the green stripes on vegetable bag denote? When you go out for grocery shopping and go towards the veggies section, the fresh produce is often stored in bags with horizontal green stripes. These green stripes are often thin and almost unnoticeable. Have you ever noticed and wondered about their purpose? These green […] . . Latest IND . . . . #trendingnews #newstrending #trendingtopicnews #lifestyle #business #news #healthylifestyle #smallbusiness #supportsmallbusiness #lifestyleblogger #luxurylifestyle #businessowner #businesswoman #smallbusinessowner #businessnews

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Lesser-known benefits of Mango Peels Full Article Link >>> https://lnkd.in/duyZZfRj Summer season is here and it’s time to immerse the soul in the goodness of sweet, tangy Mangoes. There’s no denying that Mangoes are one of the most loved fruits around the world and there’s nothing more delightful than gorging on delicious Mangoes, but wait do you also throw away the peels? Well, you need […] . . Latest IND . . . . #trendingnews #newstrending #trendingtopicnews #lifestyle #business #news #healthylifestyle #smallbusiness #supportsmallbusiness #lifestyleblogger #luxurylifestyle #businessowner #businesswoman #smallbusinessowner #businessnews

🚀 Day 43 System Design & DSA Journey Update 🌐💻 Today's exploration delved deep into problems based on binary search. Here's a recap of the problems I've tackled and the approaches I've explored: 1️⃣ Find Peak Element:    - Brute Force Approach: Utilizing linear search to find the peak element.      - Time Complexity: O(n)      - Space Complexity: O(1)    - Optimal Approach: Leveraging binary search to efficiently locate the peak element.      - Time Complexity: O(log n)      - Space Complexity: O(1)    - Intuition: The optimal approach leverages the property that a peak element, in a given array, will always be located where the ascending sequence turns into a descending one. By iteratively narrowing down the search space through binary search, we efficiently locate the peak element. 2️⃣ Find Nth Root Of M:    - Brute Force Approach: Employing linear search to find the nth root of m.      - Time Complexity: O(m)      - Space Complexity: O(1)    - Optimal Approach: Utilizing binary search for an efficient search for the nth root of m.      - Time Complexity: O(log m)      - Space Complexity: O(1)    - Intuition: In the optimal approach, we exploit the monotonic nature of the root function. By iteratively narrowing down the search space using binary search, we efficiently converge to the desired nth root of m. 3️⃣ Koko Eating Bananas:    - Brute Force Approach: Using linear search to determine the minimum rate to eat bananas.      - Time Complexity: O(*max(arr) * n)      - Space Complexity: O(1)    - Optimal Approach: Employing binary search to efficiently find the minimum rate to eat bananas.      - Time Complexity: O(*max(arr) * n)      - Space Complexity: O(1)    - Intuition: By realizing the monotonic nature of the eating rate function and the problem's objective, we apply binary search to efficiently converge to the minimum rate at which Koko can eat bananas within the given time constraints. 4️⃣ Rose Garden:    - Brute Force Approach: Utilizing linear search to find the minimum number of days required to make bouquets.      - Time Complexity: O(*max(arr) * n)      - Space Complexity: O(1)    - Optimal Approach: Leveraging binary search to efficiently find the minimum number of days required to make bouquets.      - Time Complexity: O(*max(arr) * n)      - Space Complexity: O(1)    - Intuition: Recognizing the monotonic relationship between the number of days and the feasibility of making bouquets, binary search efficiently converges to the minimum number of days required, ensuring the optimal solution. Solutions: https://lnkd.in/g9KGrPK7 Each problem was approached with both brute force and optimal strategies, highlighting the efficiency gains achieved through binary search algorithms. By honing these problem-solving skills, I'm enhancing my ability to tackle diverse challenges in algorithmic programming. #DSA #BinarySearch #ProblemSolving #Optimization #Algorithms

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