GENAI-PYTHON.AU1
Generative AI Apps with LangChain and Python
Learn LangChain, Python, RAG, prompt engineering, and AI agents through hands-on projects designed for real deployment.
- Practice in 31 Laboratorios prácticos — nothing to install
- 11 Lecciones interactivas y 75 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
31 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
11Lecciones interactivas
75Topics
31Laboratorio en vivo
11Vídeos
96Tarjetas didácticas
96Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
This course cuts through the hype, teaching you to build robust Generative AI applications with LangChain and Python. We'll start with integrating LLM APIs, then move to practical Q&A and chatbot construction. You'll explore various LLM models, master prompt engineering for effective outputs, and understand the critical role of LangChain Chains in complex workflows. We'll dive deep into Retrieval-Augmented Generation (RAG) for advanced search, then build and deploy real-world agents. Expect to confront common pitfalls like prompt injection and model hallucination, learning to mitigate them. This isn't about theoretical perfection; it's about building functional, deployable AI.
- Architect and implement Generative AI applications using LangChain, integrating various LLM APIs effectively while managing API rate limits and cost implications.
- Design and build robust Q&A systems and conversational chatbots, understanding the trade-offs between simple prompt-based and complex chain-driven interactions.
- Master prompt engineering techniques, including few-shot prompting and output parsing, to control LLM behavior and mitigate common issues like hallucination or irrelevant responses.
- Develop and deploy advanced agent-based applications and Retrieval-Augmented Generation (RAG) systems, navigating the complexities of document loading, text splitting, and vector store integration for enhanced accuracy.
Course Highlights
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11 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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31 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
11 Lecciones interactivas · 75 topics01 Introduction to LangChain and LLMs 8 topics · 1 Laboratorio en vivo +
- Understanding LangChain
- Why is LangChain Important?
- Real-World Examples of LangChain
- Integrating LLMs with LangChain
- Exploring Core Components of LangChain
- LLM Application Development Workflow
- Key Takeaways
- Looking Ahead
1 Laboratorio en vivo in this lesson — see the labs panel →
02 Integrating LLM APIs with LangChain 5 topics · 3 Laboratorio en vivo +
- Understanding LLM APIs
- Using Direct LLM API vs. LangChain
- Preparing Your Dev Environment
- Exercise 1: Calling an LLM API Directly
- Key Takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
03 Building Q&A and Chatbot Apps 10 topics · 4 Laboratorio en vivo +
- LangChain Framework Components
- LangChain Ecosystem
- Using LangChain Models with LLMs
- Building a Simple Q&A Application
- Building a Conversational App
- Difference Between the Q&A and Chatbot Example
- Error Handling and Troubleshooting
- Development Playground
- Maximize Your Learning Through Experimenting
- Key Takeaways
4 Laboratorio en vivo in this lesson — see the labs panel →
04 Exploring Large Language Models (LLMs) 6 topics · 1 Laboratorio en vivo +
- OpenAI’s Models
- Google’s AI Model Overview
- Anthropic’s Claude AI Models
- Overview of Cohere AI Models
- Meta AI Models
- Key Learnings
1 Laboratorio en vivo in this lesson — see the labs panel →
05 Mastering Prompts for Creative Content 8 topics · 4 Laboratorio en vivo +
- Importance of Prompt Engineering
- Prompt Engineering Steps
- Components of a Prompt
- Few-Shot Prompt Template
- Output Parsers
- ChatPrompt Templates
- Case Study: Streamlining Customer Service
- Key Takeaways
4 Laboratorio en vivo in this lesson — see the labs panel →
06 Building Intelligent Chatbots and Automated Analysis Systems Using Chains 13 topics · 5 Laboratorio en vivo +
- Introduction to LangChain Chains
- Understanding the Components of Chains
- Types of Chains
- Difference Between LCEL and Legacy Chains
- When to Use Different Types of Chains
- Building with LCEL Chains
- Types of LCEL Chains
- Building with Legacy Chains
- Types of Legacy Chains
- Building Real-World Apps with Legacy Chains
- More Complex Workflow Apps Using Chain Composition Strategies
- Advanced Chain Techniques
- Key Takeaways
5 Laboratorio en vivo in this lesson — see the labs panel →
07 Building Advanced Q&A and Search Applications Using Retrieval-Augmented Generation (RAG) 10 topics · 3 Laboratorio en vivo +
- Importance of RAG
- How Does RAG Work?
- Document Loaders
- Text Splitters
- Vector Stores
- Text Embedding Models
- Retrievers
- Indexing
- Key Takeaways
- References
3 Laboratorio en vivo in this lesson — see the labs panel →
08 Your First Agent App 4 topics · 2 Laboratorio en vivo +
- Introduction
- Why Agents Matter
- Your First End-to-End Working Agent App
- Key Takeaways
2 Laboratorio en vivo in this lesson — see the labs panel →
09 Building Different Types of Agents 4 topics · 4 Laboratorio en vivo +
- Learning Objectives
- Designing and Implementing an Agent
- Agent Types
- Key Takeaways
4 Laboratorio en vivo in this lesson — see the labs panel →
10 Projects: Building Agent Apps for Common Use Cases 3 topics · 3 Laboratorio en vivo +
- Creating a Custom Agent
- Practical Use Cases for Agents
- Key Takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
11 Building and Deploying a ChatGPT-like App Using Streamlit 4 topics · 1 Laboratorio en vivo +
- Setting Up Your Development Environment
- Building the Streamlit LangChain UI App
- Deploying the LangChain Application
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
31 Laboratorio en vivos- Building a Simple Generative App Using LangChain
- Building a Real-Time Customer Service Chatbot
- Building a Content Generation Platform with LangChain
- Calling an LLM API Using Python
- Using LangChain for a Retrieval Task
- Building a Simple Q&A Application
- Building a Conversational App Using Python
- Exploring Prompt Engineering with OpenAI Playground
- Generating Custom Images from Text Prompts
- Creating a Custom Example Selector from Scratch
- Crafting a Few-Shot Prompt Template for Question Answering
- Building Structured Movie Data Using PydanticOutputParser
- Creating a Chat Prompt Template
- Building and Executing LCEL Chains
- Building Conversational Apps with ConversationChain
- Building a Data Summarization App with Sequential Chains
- Building a Sentiment Analysis App Using Conditional Chains
- Handling Large Datasets with Chains
- Building a Data Ingestion Pipeline for RAG
- Using CharacterTextSplitter for Basic Text Chunking
- Generating and Analyzing Text Embeddings
- Generating Content Using AI Agents
- Building an End-to-End Agent Application
- Managing Agent Inputs and Outputs in LangChain
- Building a Structured Chat Agent
- Building a Zero-Shot ReAct Agent
- Implementing Tool-Calling Agents in LangChain
- Creating a Custom Agent in LangChain
- Creating a Recommendation Engine Using LLM Agents
- Building Decision-Making Agent for Live Data Streams
- Running and Testing Streamlit Applications Locally
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What is LangChain and why is it crucial for building Generative AI applications?+
LangChain is a framework designed to simplify the development of applications powered by large language models (LLMs). It's crucial because it provides modular components and chains to manage complex interactions, integrate external data sources, and build sophisticated applications like chatbots and agents more efficiently than direct API calls, despite its own learning curve.
How does this course help me integrate different Large Language Models (LLMs) into my applications?+
This course covers integrating various LLM APIs, including those from OpenAI, Google, Anthropic, and Meta. You'll learn the practical steps for setting up your development environment, making direct API calls, and leveraging LangChain's abstractions to switch between different models, understanding the performance and cost trade-offs of each.
What are the practical benefits of mastering prompt engineering for Generative AI?+
Mastering prompt engineering is critical for controlling LLM behavior. You'll learn to craft effective prompts, utilize few-shot templates, and implement output parsers to achieve desired responses, reduce hallucinations, and ensure your Generative AI applications deliver consistent, relevant, and structured outputs, avoiding common failure points of vague instructions.
Will I learn to build and deploy real-world Generative AI agents and RAG systems?+
Absolutely. The course dedicates significant sections to building advanced Q&A and search applications using Retrieval-Augmented Generation (RAG), and developing various types of agents. You'll learn to create custom agents for common use cases and deploy a ChatGPT-like application using Streamlit, understanding the practical challenges of deployment and scaling.
How will I handle common challenges like LLM hallucination or irrelevant outputs in my applications?+
The course directly addresses these challenges through prompt engineering techniques, output parsers, and the implementation of Retrieval-Augmented Generation (RAG). You'll learn strategies to ground LLM responses in factual data, guide their behavior, and design systems that are more robust against generating incorrect or irrelevant information, acknowledging that complete elimination is often impractical.
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- 1 año de acceso completo
- 31 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito