AI/ML

Testing RAG Pipelines

In today's world, where Retrieval-Augmented Generation (RAG) pipelines are increasingly integrated into applications, it's more important than ever to thoroughly test these systems.Large Language Models (LLMs) are powerful but not perfect. They can hallucinate—generating information that isn’t in the context—and sometimes provide answers that are irrelevant to the user’s query. These issues, or "smells," can harm the reliability of your application, making it essential to thoroughly test your LLM before and after deployment.As open-source LLMs continue to surge in popularity,...

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Query Decomposition: Understanding the User's Perspective

While searching for any information on the Internet, it's important to determine whether the results being fetched are relevant to what is being searched for. Most of us use search engines like Google, Bing, Yahoo!, or DuckDuckGo to find answers, but do these search engines always return the same results? Interestingly enough, that is not the case. Let's uncover the mystery behind why the same search term can yield different results across different platforms.For example, if the user is searching...

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Exploring LoRA - Part 2: Analyzing LoRA through its Implementation on an MLP

Source: ChatGPT+ Part 1 delves into the concept and necessity of fine-tuning pre-trained large models for specialized tasks. It introduces the conventional method of fine-tuning, where only the top layers of the model are adjusted, and highlights its limitations, particularly in terms of computational and storage demands. To address these challenges, the article shifts focus to Parameter-Efficient Fine-Tuning (PEFT) methods, specifically the use of adapter modules, as proposed by Houlsby and colleagues. These adapters are small, inserted layers that allow for...

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Exploring LoRA — Part 1: The Idea Behind Parameter Efficient Fine-Tuning and LoRA

Source: ChatGPT+ Pre-trained large language models undergo extensive training on vast data from the internet, resulting in exceptional performance across a broad spectrum of tasks. Nonetheless, in most real-world scenarios, there arises a necessity for the model to possess expertise in a particular, specialized domain. Numerous applications in the fields of natural language processing and computer vision rely on the adaptation of a single large-scale, pre-trained language model for multiple downstream applications. This adaptation process is typically achieved through a...

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Soaring to New Heights: AI, Machine Learning, and Data Strategy in the Airline Industry

Written by Ravindrababu T and mckimmer In the fiercely competitive airline industry, the race to deliver a frictionless passenger experience is on. Air travel, once merely about getting from A to B, is now a journey laden with potential touch-points where airlines can either win loyalty or lose business. The key to success lies in leveraging machine learning, artificial intelligence (AI), and a robust data strategy to not only streamline this journey but also to unlock avenues for increased revenue. The application of...

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Data-Driven Customer Insights: A New Altitude for Airline Revenue Management and Retail Pricing

The aviation industry, with its dynamic pricing and complex service delivery, presents a fertile ground for AI driven data insights to revolutionize customer experience and revenue management. Understanding customer behavior through meticulous data mining is not just a competitive advantage; it’s a strategic imperative that is increasingly separating the successful airlines from those struggling to achieve profitability. The crux of the matter lies in mining and interpreting the vast array of customer data at each touchpoint — from booking a...

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That state-of-the-art LLM embedding may not work for your use case — here is why

With LLM embedding-based applications, it’s quite easy to understand and apply semantic meaning in machine learning algorithms. For example, you can use text embedding to feed to a classifier for rating user sentiment on user feedback or use it for RAG-based semantic search, etc. We understand words by mapping them in different dimensions. For example, relative terms such as good, better and best or low carb vs high carb food. If we represent the word by rating these values in...

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How to Adopt LLMs in your business: A Complete Guide

It is a deceptively simple construct — an LLM(Large language model) is trained on a huge amount of text data to understand language and generate new text that reads naturally. The model is then able to execute simple tasks like completing a sentence “The cat sat on the…” with the word “mat”. Or one can even generate a piece of text such as a haiku to a prompt like “Here’s a haiku:” But it gets even cooler! Once an LLM...

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Generative AI in Software Architecture: Don’t Replace Your Architects Yet!

This article is part of a series where we explore how Generative AI can be utilized for the various stages in the software development lifecycle. You can read this post about the methods and tools used for experimentation.Software architecture is essential when building software; it ensures that the developed software satisfies the business needs and sets up technology teams for success. This article explores how Generative AI, specifically GPT, can assist in software architecture.Software Architecture is typically handled by engineers with different...

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Enhancing Domain-Driven Design with Generative AI

Creating robust and maintainable applications that align closely with business requirements is a constant challenge in software development. Domain-Driven Design (DDD) has been quite successful in tackling this challenge. By placing the domain at the heart of the development process, DDD empowers developers to create highly scalable, modular, and business-oriented solutions. In this article, we will examine the potential of using Generative AI in Domain Driven Design. In our experiment, we developed a time-tracking application using Generative AI tools to...

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