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Build with LLMs: Prompt Engineering & Real AI Projects · LearnSpace
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Build with LLMs: Prompt Engineering & Real AI Projects

Курс от Packt
Средний≈ 8.9 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. This intermediate-level course dives deep into the essential techniques for working with large language models (LLMs), providing you with the practical tools and knowledge needed to build powerful AI applications. You will learn core concepts such as tokenization, log probabilities, and context windows, and gain hands-on experience through a series of engaging labs and projects. Additionally, you'll explore foundational prompt engineering patterns to improve response accuracy, control output formats, and develop advanced techniques such as few-shot prompting and chain-of-thought prompting. The course is structured to guide you from understanding the theoretical underpinnings of LLMs to applying those concepts in real-world scenarios. As you progress, you will work on an exciting project, building an AI-powered Git commit message generator. Through this project, you'll gain valuable experience in designing prompt templates, creating core logic for AI commits, and enhancing functionality with user reviews and model selection. Each section includes practical labs and exercises that ensure you're not just learning the theory, but also building real skills. The course is perfect for intermediate learners looking to enhance their AI development capabilities, particularly those interested in applying prompt engineering to optimize model behavior. A solid understanding of programming and LLMs is beneficial, but anyone with a technical background will find this course accessible. By the end of the course, you will be able to design structured prompts, implement advanced prompting techniques, generate AI-driven Git commit messages, and effectively manage API usage and costs.

Навыки, которые вы освоите

Prompt EngineeringGit (Version Control System)Token OptimizationPrompt PatternsLarge Language ModelingAI WorkflowsLLM ApplicationApplication Programming Interface (API)Generative AIAI Integrations

Программа курса

3 модулей · 52 учебных материалов

01Core Concepts: The Inner Workings of LLMs17 материалов

Core Concepts: The Inner Workings of LLMs

Introduction to the Course 'Build with LLMs: Prompt Engineering & Real AI Projects'ЧтениеFull Specialization ResourcesЧтениеSection OverviewВидеоUnderstanding TokenizationВидео

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Packt - Course Instructors

Преподаватель курса

Build with LLMs: Prompt Engineering & Real AI Projects
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 8.9 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Пушту, Индонезийский, Испанский, Дари, Японский

Часть программы вашего университета
Practical Lab: Exploring TokenizationВидео
Practical Lab: Advanced Tokenization ConceptsВидео
The Role of Log Probabilities in Text GenerationВидео
Practical Lab: Simulating Sentence GenerationВидео
The Context Window and Its LimitationsВидео
Key Components of API Usage CostsВидео
Practical Lab: Calculating API CostsВидео
Practical Lab: Advanced Cost AnalysisВидео
Exploring Different Classes of Language ModelsВидео
The Importance of System, User, and Assistant RolesВидео
Practical Lab: Crafting Effective System PromptsВидео
Exploring Tokenization, Roles, and Cost in LLM ConversationsDIALOGUE
Core Concepts: The Inner Workings of LLMs - AssessmentЗадание
02Foundational Prompt Engineering Patterns20 материалов

Foundational Prompt Engineering Patterns

Section OverviewВидеоThe Anatomy of a Prompt: Instructions, Context, and ConstraintsВидеоPractical Lab: Building a Structured PromptВидеоUsing Delimiters to Structure PromptsВидеоRefactoring a Prompt with DelimitersВидеоPractical Lab: Applying Delimiters EffectivelyВидеоSetting Personas for Targeted ResponsesВидеоPractical Lab: Implementing the Persona PatternВидеоPractical Lab: Setting Clear Behavioral Guidelines for the AIВидеоCase Study: Creating a Database Administrator PersonaВидеоImproving Accuracy with Few-Shot PromptingВидеоPractical Lab: Implementing Few-Shot ExamplesВидеоStrategies for Controlling Output FormatВидеоAdvanced Output Formatting TechniquesВидеоEncouraging Reasoning with Chain-of-ThoughtВидеоPractical Lab: Applying Chain-of-Thought PromptingВидеоOrganizing Information with the Template PatternВидеоPractical Lab: Building and Using Prompt TemplatesВидеоMastering Core Prompt Patterns and Structured OutputDIALOGUEFoundational Prompt Engineering Patterns - AssessmentЗадание
03Project Module #2: AI-Powered Git Commit Messages15 материалов

Project Module #2: AI-Powered Git Commit Messages

Module Overview and GoalsВидеоSetting Up the Boilerplate for the Commit FeatureВидеоProgrammatically Retrieving Git DiffsВидеоDesigning Prompt Templates for Commit MessagesВидеоBuilding the Core Logic for AI CommitsВидеоAdding a User Review and Edit StepВидеоEnhancing the Test Suite for the Commit FeatureВидеоIntegrating Robust LoggingВидеоEnabling Model Selection via the CLIВидеоDocumenting the Smart Commit FeatureВидеоImplementing an Interactive AI-Powered Commit CommandDIALOGUEConclusion to the Course 'Build with LLMs: Prompt Engineering & Real AI Projects'ЧтениеProject Module #2: AI-Powered Git Commit Messages - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание