Building RAG Applications with LangChain and Gen AI Practice Exam
Building RAG Applications with LangChain and Gen AI Practice Exam
About Building RAG Applications with LangChain and Gen AI Exam
This exam focuses on the principles and techniques required to build Retrieval-Augmented Generation (RAG) applications using LangChain and Generative AI (Gen AI) technologies. RAG combines the power of large language models (LLMs) with document retrieval systems to provide more accurate, context-driven outputs, ideal for applications such as question answering, document summarization, and knowledge extraction.
Skills Evaluated
The exam will test your ability to integrate various modules of LangChain, utilize Gen AI to enhance information retrieval processes and optimize model performance for real-world applications. You will be evaluated on your ability to implement and deploy RAG systems that can extract data from documents and databases, generate responses based on contextual information, and manage the flow of information effectively.
Skills Required
- Understanding of basic and advanced Python programming concepts is essential, especially for integrating LangChain and Gen AI tools into applications.
- Familiarity with LangChain’s framework for chaining large language models (LLMs) with external data sources, such as databases or APIs.
- Understanding of how Generative AI works, including the ability to fine-tune models and create context-driven outputs for RAG applications.
- The ability to integrate various APIs for retrieving information, enhancing model responses, and facilitating smooth data flow between the model and the user interface.
- Knowledge of how to store, retrieve, and manage large datasets, ensuring efficient document retrieval for contextual accuracy.
- Skills in optimizing models for better accuracy, speed, and resource management during both training and deployment.
Who should take the Exam?
This exam is intended for developers, data scientists, AI engineers, and machine learning practitioners who are looking to specialize in building sophisticated AI applications, particularly those leveraging RAG techniques. It is suitable for those who have:
- A foundational knowledge of AI and machine learning and want to explore the specific application of LangChain and RAG architectures.
- Experience with Python programming and are looking to apply it to cutting-edge AI technologies, especially for natural language processing (NLP) and document-based applications.
- Familiarity with Generative AI and are interested in learning how to integrate it with retrieval systems to create more intelligent, context-aware applications.
- Interest in developing AI-powered applications for industries such as healthcare, finance, education, and customer service, where access to accurate, contextual data is critical.
Course Outline
The Building RAG Applications with LangChain and Gen AI Exam covers the following topics -
Domain 1 - Course Overview
- Introduction to the Course Content
- Understanding Large Language Models (LLMs)
- Introduction to LangChain Framework
- Fundamentals of Prompting Techniques
- Setting Up the Development Environment
- Installing Necessary Dependencies
- Using Google Gemini LLM in Place of OpenAI GPT
- Code Demonstration - Simple Prompt Creation and Model Chaining
Domain 2 - Core Concepts in LangChain
- Getting Started with Prompt Templates and Chat Prompts
- Working with Agents and Their Tools
- Advanced Usage of Agents and Tools
- Introduction to Document Loaders and Splitters
- Understanding Output Parsers
- Exploring Language Embeddings and Vector Databases
- Developing Your First RAG Application Using a Vector Database
- Chain Types - Stuff, Map-Reduce, and Refine
- Understanding LCEL (LangChain Expression Language)
- Building Your First LangChain Application
Domain 3 - Building RAG Applications and Projects
- Creating a RAG Application with SQL Data
- Integrating Conversational Memory in RAG Applications
- Building a CV Upload and Search Application
- Developing a Website Query Chatbot Project
- Analyzing Structured Data from CSV/Excel Files Using Natural Language
- Building an Invoice Extraction RAG Application
- Trace and Evaluation Techniques with LangSmith
- Capstone Project for Practical Application
