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Advanced Prompting & AI Tooling · LearnSpace
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Advanced Prompting & AI Tooling

Курс от Packt
Продвинутый≈ 10.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this advanced course, you'll deepen your expertise in prompt engineering and learn how to craft highly effective prompts for sophisticated AI models. The course covers a range of advanced techniques, such as the "Flip the Script" pattern, self-consistency, function calling, and more. With practical labs, you’ll experiment with these techniques, refining AI-generated prompts, and building more dynamic, flexible, and high-performing AI systems. You'll also dive into function calling and applying it to real-world tasks, as well as improving response quality through decomposition and self-critique. The course also includes a comprehensive project where you will build an AI-powered code reviewer, allowing you to apply your prompt engineering skills in a practical setting. Throughout the project, you’ll enhance the tool with features like Git integration, code logic and syntax checking, self-critique, and the creation of expert personas. The project will culminate with the migration to structured output, improving the tool’s data management and its interaction with other systems. This course is ideal for learners who have a solid understanding of AI models and prompt engineering, and wish to take their skills to the next level by designing more powerful, efficient, and customized AI-driven tools. The course requires experience in programming and basic familiarity with AI principles. By the end of the course, you will be able to build sophisticated AI-powered tools, refine and optimize prompts for complex tasks, and integrate advanced techniques like function calling and self-consistency into your AI systems.

Навыки, которые вы освоите

MaintainabilityJSONSoftware DocumentationPrompt EngineeringTool CallingCode ReviewPersona DevelopmentPrompt PatternsData ManagementLarge Language ModelingGenerative AIGit (Version Control System)

Программа курса

3 модулей · 54 учебных материалов

01Mastering Advanced Prompt Engineering15 материалов

Mastering Advanced Prompt Engineering

Introduction to the Course 'Advanced Prompting & AI Tooling'ЧтениеFull Specialization ResourcesЧтениеSection OverviewВидеоPractical Lab: The "Flip the Script" PatternВидео

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Packt - Course Instructors

Преподаватель курса

Advanced Prompting & AI Tooling
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Обучение на Coursera

≈ 10.1 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Испанский, Японский

Часть программы вашего университета
Practical Lab: "Flip the Script" Pattern ApplicationsВидео
Practical Lab: Using AI to Generate PromptsВидео
Practical Lab: Refining AI-Generated PromptsВидео
Practical Lab: Breaking Down Complex Tasks with DecompositionВидео
Practical Lab: Improving Responses with Self-CritiqueВидео
Practical Lab: Introduction to Function CallingВидео
Practical Lab: Advanced Function CallingВидео
Practical Lab: Introduction to Self-ConsistencyВидео
Practical Lab: Self-Consistency Wrap-UpВидео
Mastering Prompt Engineering Patterns with LLMsDIALOGUE
Mastering Advanced Prompt Engineering - AssessmentЗадание
02Project Module #3: Building an AI Code Reviewer22 материалов

Project Module #3: Building an AI Code Reviewer

Module Overview and GoalsВидеоReviewing the Module Implementation PlanВидеоRefactoring to Use the GitPython LibraryВидеоImproving Exception HandlingВидеоCreating the Boilerplate for the Review CommandВидеоUsing Dataclasses for Structured DataВидеоAdding the Review Command to the CLIВидеоDesigning Prompts for Logic and Syntax ChecksВидеоExecuting the Core AI Review LogicВидеоDeveloping Expert Personas for Deeper Code AnalysisВидеоBuilding a Self-Consistency Workflow for ReviewsВидеоCompleting the Self-Consistency ImplementationВидеоDefining External Tools for the ReviewerВидеоBuilding a Basic Tool RegistryВидеоCompleting the Tool RegistryВидеоResolving Static Typing ErrorsВидеоCreating the Initial AI Tool-Calling LoopВидеоRefining the Tool-Calling LogicВидеоWriting Tests for the Tool-Calling FeatureВидеоIntegrating a Self-Critique Phase into the Review ProcessВидеоDesigning Multi-Persona AI Code Review PipelinesDIALOGUEProject Module #3: Building an AI Code Reviewer - AssessmentЗадание
03Project Module #4: Structured Output and Finalization17 материалов

Project Module #4: Structured Output and Finalization

Module Overview and GoalsВидеоInitiating the Migration to Structured OutputВидеоRefactoring the Review Module for MaintainabilityВидеоResolving Test Failures After RefactoringВидеоUpdating Prompts to Generate JSON OutputВидеоModifying Tests to Validate JSON OutputВидеоAdapting the Pipeline to Use DataclassesВидеоContinuing the Dataclass MigrationВидеоFinalizing the Pipeline MigrationВидеоAddressing and Fixing Minor BugsВидеоCorrecting Remaining Test FailuresВидеоBuilding the JSON Output ParserВидеоFinal Touches: Synthesis Logic and DocumentationВидеоConclusion to the Course 'Advanced Prompting & AI Tooling'ЧтениеProject Module #4: Structured Output and Finalization - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание