book

ChatGPT for Conversational AI and Chatbots

by Adrian Thompson

July 2024

Intermediate to advanced

250 pages

6h 29m

English

Packt Publishing

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Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
What are chatbots and conversational AI?A brief history of conversational AIAn overview of chatbots and automated assistantsEvolution of chatbots and conversational AIUnderstanding conversational AI applicationsCustomer serviceLanguage translationEducationHealthcareBanking and insuranceRetailHuman resourcesConversational AI as a training tool – the emergence of digital humansConclusionWhat is OpenAI’s ChatGPT?Understanding the large language technology behind ChatGPTThe role of tokens in LMsUnderstanding OpenAI’s LMsConclusionCapabilities and applications of ChatGPTCapabilities of ChatGPTHow smart is ChatGPT?Applications of ChatGPTLimitations of ChatGPTLimitationsRisks and securitySummaryFurther reading
Technical requirementsUnderstanding conversation designExploring the role of conversation designersWorking with practical applications of ChatGPT in conversation designIntent clustering with ChatGPTUnderstanding utterance and entity generationUsing ChatGPT to help write your dialoguePersona creation with ChatGPTUsing ChatGPT for user researchCreating our user and chatbot personasSimulating conversationsWhat is a sample dialogue?Creating sample dialogueCreating sample dialogue with personasTesting and iteration in conversation design with ChatGPTTesting with ChatGPTSummaryFurther reading
Technical requirementsMastering the ChatGPT interfaceThe Free and Plus versions of ChatGPTChatGPT interfaceGPTsExploring OpenAI PlaygroundGetting startedUI featuresPricing for API and PlaygroundLearning to use the ChatGPT APIGetting startedCalling the API directlySetting up with the OpenAI Python librarySetting up with the OpenAI Node.js libraryOther ChatGPT librariesSummaryFurther reading
Technical requirementsGoing through the concepts of prompt engineeringUnderstanding the core components of a successful promptInstructionContextScopeRoleAudienceInput dataOutput dataConclusionWorking with a prompt engineering strategyDefine clear goalsEmploy iterative prompt developmentStart simpleUse follow-up prompts to test against multiple examplesUse temperature when you need toHandling memory limitations in ChatGPTConclusionKnowing the prompt engineering techniquesFew-shot learning for a customer support chatbotPrompting to summarize data for a conversational agentPrompting to create your own chatbot powered by ChatGPTSummaryFurther reading
Technical requirementsIntroduction to LangChainLangChain librariesCore components of LangChainWorking with LLMs in LangChainPrompt templatesUsing output parsersUnderstanding LangChain Expression LanguageWhat is LCEL?Key components of LCELRunnable protocolA simple example of LCELCreating different LangChain chainsBasic chain exampleCreating a sequential chain to investigate conversational dataUtilizing parallel chains in LangChain for efficient multi-source information gatheringRouting chains to answer questions effectivelySummaryFurther reading
Technical requirementsDebugging and monitoring LangChainUnderstanding tracing techniquesIntroducing LangSmithLeveraging LangChain agentsWhat is an agent?What are LangChain tools?An introduction to OpenAI tool callingPlug-and-play LangChain tools for immediate integrationAn out-of-the-box tool exampleUsing our tool with an agentCreating a custom weather toolExploring LangChain memoryExploring the different types of memory applicationsUnderstanding memory challengesIntroducing memory usage techniquesUnderstanding an example of using memory in LangChainSummaryFurther reading

Technical requirementsWhy do we need RAG?Understanding the steps needed to create a RAG systemDefining your RAG data sourcesPreprocessing our content and generating embeddingsChunking for effective LLM interactionsCreating embeddings with OpenAI modelsStoring and searching our embeddings with a vector storeDeciding which vector database to useWorking through a RAG example with LangChainIntegrating data – choosing your document loaderCreating manageable chunks with text splittingCreating and storing text embeddingsBringing everything together with LangChainSummaryFurther reading
Technical requirementsScoping our ChatGPT projectA holiday assistant use caseA persona outline for Ellie the explorerEllie’s conversational scopeTechnical featuresGetting our data ready for the chatbotSelecting our data sourcesPreparing the hotel dataCreating our agent for complex interactionsCreating the agent toolsBringing it all together – building Your own LangChain Chatbot with StreamlitCreating secrets and config managementCreating our agent serviceBuilding our Streamlit chat appRunning and testing Ellie, our chatbot applicationWays to improve EllieSummaryFurther reading
Technical requirementsGoing into productionUnderstanding the dangers of going into productionChallenges of RAG systemsEvaluating production systemsComponents of an evaluation systemLearning how to use LangSmith to evaluate your projectApplication monitoring in production with LangSmithAdvanced monitoring features with LangSmithTracing and data managementMonitoring toolsAdvanced monitoring featuresAlternatives to ChatGPT and LangChainSome alternatives to ChatGPT and OpenAI LLMsSome alternatives to LangChainLooking at the growing LLM landscapeThe growth of the small language model (SLM)Are LLMs reaching their limit?Enter SLMsSLMs versus LLMs – key differencesAdvantages of SLMsDisadvantages of SLMsSome examples of SLMsThe transformative potential of SLMsWhere to go from hereSummaryFurther reading
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Overview

If you want to create cutting-edge conversational applications using the latest AI technologies, 'ChatGPT for Conversational AI and Chatbots' is the perfect guide for you. This book walks you through the fundamentals of large language models like ChatGPT, and leads you into advanced topics such as LangChain and data integration.

What this Book will help me do

Understand the capabilities and applications of ChatGPT for creating conversational AI.
Learn how to design effective conversation flows and utilize ChatGPT API for development.
Master advanced LangChain techniques to optimize your chatbot solutions.
Develop chatbots that can integrate with external or private data for tailored interactions.
Stay ahead by exploring future developments in conversational AI technologies.

Author(s)

Adrian Thompson has over a decade of experience in artificial intelligence and software development. He has worked on multiple AI projects in various industries and is an advocate for accessible technologies. With a knack for breaking down complex topics, Adrian provides readers with clear, actionable insights and practical guidance.

Who is it for?

This book is ideal for software engineers, data scientists, or tech enthusiasts seeking to build conversational AI technologies. Beginners and intermediate users interested in ChatGPT applications will find valuable insights. A basic understanding of coding and conversational design concepts is recommended.