Designing Large Language Model Applications

By healtycares On Aug 24, 2025

Designing Large Language Model Applications You’ll take a comprehensive deep dive into the ingredients that make up a language model, explore various techniques for customizing them such as fine tuning, learn about application paradigms like rag (retrieval augmented generation) and agents, and more. But transitioning from demos and prototypes to full fledged applications can be difficult. this book helps close that gap, providing the tools, techniques, and playbooks that practitioners need to build useful products that incorporate the power of language models.

Designing Large Language Model Applications In the first chapter, i introduce the concept of a language model and show why next token prediction is such a powerful paradigm. i also provide a brief history of llms and describe how we got to this stage. Designing large language model appl ications i s a complete, up to date guide on the concepts and techniques behind researching, designing, and bui lding large language model applications. Learn how to build production grade llm applications. a hands on guide for developers covering rag, fine tuning, agents, and performance optimization. Experienced ml researcher suhas pai offers practical advice on harnessing llms for your use cases and dealing with commonly observed failure modes.

5 Large Language Model Applications To Use For 2024 Learn how to build production grade llm applications. a hands on guide for developers covering rag, fine tuning, agents, and performance optimization. Experienced ml researcher suhas pai offers practical advice on harnessing llms for your use cases and dealing with commonly observed failure modes. Suhas pai discusses various aspects of large language models and machine learning research, including prompt engineering, model selection, prompting in deployed applications, challenges in deploying gpt based tasks, estimating token probabilities, determining correctness of answers, token frequency in pre training data sets, limitations of retri. Moving ahead, with a focus on the python based, lightweight framework called langchain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using llms and powerful toolkits. With this book, you'll learn the tools, techniques, and playbooks for building useful products that incorporate the power of language models. Experienced ml researcher suhas pai provides practical advice on dealing with commonly observed failure modes and counteracting the current limitations of state of the art models. you'll take a comprehensive deep dive into the transformer architecture and its variants.

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Building AI Applications with Large Language Models

Building AI Applications with Large Language Models

Building AI Applications with Large Language Models How Large Language Models Work Stanford CS229 I Machine Learning I Building Large Language Models (LLMs) Large Language Models explained briefly Five Steps to Create a New AI Model Create a Large Language Model from Scratch with Python – Tutorial Build a Large Language Model AI Chatbot using Retrieval Augmented Generation Introduction to Large Language Models (LLMs) Week 5 | NPTEL ANSWERS 2025 #nptel2025 #myswayam #nptel What is Retrieval Augmented Generation (RAG) ? Simplified Explanation [1hr Talk] Intro to Large Language Models Simple Explanation of Large Language Models Applications | Learn Prompt Engineering What is Retrieval-Augmented Generation (RAG)? RAG vs. Fine Tuning What is an LLM? | What can I use Large Language Models for? This AI Is 100% Open Source! #497 Explained: The OWASP Top 10 for Large Language Model Applications Stanford Webinar - Agentic AI: A Progression of Language Model Usage Agentic RAG vs RAGs

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