book

Building LLM Powered Applications

by Valentina Alto

May 2024

Intermediate to advanced

342 pages

8h 45m

English

Packt Publishing

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Who this book is forWhat this book coversTo get the most out of this bookGet in touchMaking the Most Out of This Book – Get to Know Your Free BenefitsUnlock Your Book’s Exclusive BenefitsHow to unlock these benefits in three easy stepsNeed help?
What are large foundation models and LLMs?AI paradigm shift – an introduction to foundation modelsUnder the hood of an LLMMost popular LLM transformers-based architecturesEarly experimentsIntroducing the transformer architectureTraining and evaluating LLMsTraining an LLMModel evaluationBase models versus customized modelsHow to customize your modelSummaryReferences
How LLMs are changing software developmentThe copilot systemIntroducing AI orchestrators to embed LLMs into applicationsThe main components of AI orchestratorsLangChainHaystackSemantic KernelHow to choose a frameworkSummaryReferences
The most promising LLMs in the marketProprietary modelsGPT-4Gemini 1.5Claude 2Open-source modelsLLaMA-2Falcon LLMMistralBeyond language modelsA decision framework to pick the right LLMConsiderationsCase studySummaryReferences
Technical requirementsWhat is prompt engineering?Principles of prompt engineeringClear instructionsSplit complex tasks into subtasksAsk for justificationGenerate many outputs, then use the model to pick the best oneUse delimitersAdvanced techniquesFew-shot approachChain of thoughtReActSummaryReferences
Technical requirementsA brief note about LangChainGetting started with LangChainModels and promptsData connectionsMemoryChainsAgentsWorking with LLMs via the Hugging Face HubCreate a Hugging Face user access tokenStoring your secrets in an .env fileStart using open-source LLMsSummaryReferences
Technical requirementsGetting started with conversational applicationsCreating a plain vanilla botAdding memoryAdding non-parametric knowledgeAdding external toolsDeveloping the front-end with StreamlitSummaryReferences
Technical requirementsIntroduction to recommendation systemsExisting recommendation systemsK-nearest neighborsMatrix factorizationNeural networksHow LLMs are changing recommendation systemsImplementing an LLM-powered recommendation systemData preprocessingBuilding a QA recommendation chatbot in a cold-start scenarioBuilding a content-based systemDeveloping the front-end with StreamlitSummaryReferences
Technical requirementsWhat is structured data?Getting started with relational databasesIntroduction to relational databasesOverview of the Chinook databaseHow to work with relational databases in PythonImplementing the DBCopilot with LangChainLangChain agents and SQL AgentPrompt engineeringAdding further toolsDeveloping the front-end with StreamlitSummaryReferences
Technical requirementsChoosing the right LLM for codeCode understanding and generationFalcon LLMCodeLlamaStarCoderAct as an algorithmLeveraging Code InterpreterSummaryReferences

Technical requirementsWhy multimodality?Building a multimodal agent with LangChainOption 1: Using an out-of-the-box toolkit for Azure AI ServicesGetting Started with AzureCognitiveServicesToolkitSetting up the toolkitLeveraging a single toolLeveraging multiple toolsBuilding an end-to-end application for invoice analysisOption 2: Combining single tools into one agentYouTube tools and WhisperDALL·E and text generationPutting it all togetherOption 3: Hard-coded approach with a sequential chainComparing the three optionsDeveloping the front-end with StreamlitSummaryReferences
Technical requirementsWhat is fine-tuning?When is fine-tuning necessary?Getting started with fine-tuningObtaining the datasetTokenizing the dataFine-tuning the modelUsing evaluation metricsTraining and savingSummaryReferences
What is Responsible AI and why do we need it?Responsible AI architectureModel levelMetaprompt levelUser interface levelRegulations surrounding Responsible AISummaryReferences
The latest trends in language models and generative AIGPT-4V(ision)DALL-E 3AutoGenSmall language modelsCompanies embracing generative AICoca-ColaNotionMalbekMicrosoftSummaryReferences

Overview

"Building LLM Powered Applications" is the ultimate guide to understanding and utilizing large language models (LLMs) to develop intelligent and innovative apps. By diving deep into cutting-edge technologies, this book provides practical insights into embedding LLMs into real-world applications, leveraging frameworks like LangChain, and mastering essential techniques like prompt engineering.

What this Book will help me do

Understand the architecture and components of LLMs like GPT and Falcon.
Learn to leverage LangChain to integrate and orchestrate LLM components.
Develop skills in building applications enhanced with LLM-driven conversational AI.
Master the technique of fine-tuning LLMs for customized applications.
Gain awareness of ethical considerations in deploying LLM-powered solutions.

Author(s)

Valentina Alto, esteemed author and AI practitioner, brings her extensive expertise in artificial intelligence and LLM technologies to this comprehensive guide. With years of experience in both academic research and practical application development, Valentina is dedicated to making advanced AI concepts accessible and actionable for developers. Her clear, engaging writing ensures readers of diverse backgrounds can follow and apply the cutting-edge techniques covered in the book.

Who is it for?

This book is designed for software engineers, data scientists, and technical leaders eager to learn how to integrate LLMs into their projects and push the boundaries of AI application development. It is suitable for readers with foundational knowledge in machine learning and software engineering who seek practical and theory-backed guidance on LLM-powered solutions. If you're curious about large language models and their versatile applications, this book is for you.