Read more
2Month/20 Hour Price:125,000
120,000
LangChain - Develop LLM-powered applications with LangChain
This course provides an in-depth understanding of how to develop applications powered by Large Language Models (LLMs) using the LangChain framework. LangChain simplifies building AI-driven applications, particularly those leveraging LLMs such as GPT. By the end of the course, students can build end-to-end LLM-powered applications, integrate them into different systems, and optimize performance for real-world use cases.
Course Objectives:
Learn the fundamentals of LangChain and its role in building LLM-powered applicationsGain practical experience in integrating LLMs into various workflows
Develop robust, scalable applications using LangChain with real-world datasets
Implement advanced techniques like prompt engineering, chain-of-thought reasoning, and memory management for AI models
Learn to deploy LLM-powered applications for various platforms, including web and mobile
Course Content:
Module 1: Introduction to Large Language Models (LLMs) and LangChain
Overview of LLMs:Introduction to LLMs (GPT, BERT, etc.)
How LLMs work: Understanding the transformer architecture
Popular use cases of LLMs in industry
Introduction to LangChain Framework:
What is LangChain, and why use it?
LangChain vs other frameworks for building LLM applications
Setting up your development environment (Python, LangChain, OpenAI API)
Creating your first LangChain-based application
Integrating with OpenAI’s GPT API
Working with prompts and handling LLM responses
LangChain vs other frameworks for building LLM applications
Setting up your development environment (Python, LangChain, OpenAI API)
Module 2: Building Basic Applications with LangChain
LangChain Basics:Creating your first LangChain-based application
Integrating with OpenAI’s GPT API
Working with prompts and handling LLM responses
Application Example:
Building a basic chatbot with LangChain
Generating text summaries and content generation
What are Chains in LangChain?
Different types of chains: Simple chains, sequential chains, and parallel chains
Practical use of Chains to connect multiple LLM calls
Pipelines for Complex Workflows:
Generating text summaries and content generation
Module 3: Chains and Pipelines in LangChain
Understanding Chains:What are Chains in LangChain?
Different types of chains: Simple chains, sequential chains, and parallel chains
Practical use of Chains to connect multiple LLM calls
Designing and building multi-step pipelines
Use case: Combining search, generation, and reasoning steps
Handling errors and fallbacks in pipelines
Use case: Combining search, generation, and reasoning steps
Handling errors and fallbacks in pipelines
Module 4: Prompt Engineering and Optimization
Introduction to Prompt Engineering:
What is prompt engineering?
Techniques for crafting effective prompts for LLMs
Dynamic prompts and templates in LangChain
Techniques for crafting effective prompts for LLMs
Dynamic prompts and templates in LangChain
Optimizing LLM Responses:
Token efficiency and minimizing API usage
Controlling LLM responses with temperature, max tokens, and top-p
Controlling LLM responses with temperature, max tokens, and top-p
Hands-on Lab:
Building a question-answering system with optimized prompts
Module 5: Memory Management and Persistence
Memory in LangChain:
Introduction to memory types: short-term and long-term memory in LLMs
Managing state and context in applications
Managing state and context in applications
Persistent Memory Solutions:
Storing memory for continued user sessions
Integrating LangChain with databases (e.g., Redis, MongoDB) for persistent memory
Integrating LangChain with databases (e.g., Redis, MongoDB) for persistent memory
Case Study:
Building an AI assistant that remembers previous interactions
Module 6: Integrating External Tools and APIs
LangChain Toolkits:
Introduction to external tool integration
Connecting LLMs to external APIs (e.g., web scraping, calendar APIs)
Connecting LLMs to external APIs (e.g., web scraping, calendar APIs)
Real-world Integrations:
Building applications that automate workflows using LLMs
Using LangChain with third-party services (e.g., Slack, email)
Using LangChain with third-party services (e.g., Slack, email)
Hands-on Lab:
Developing an intelligent task automation application using LangChain and external APIs
Module 7: Advanced Topics in LangChain
Chain-of-Thought Reasoning:
Implementing advanced reasoning techniques
Use cases for chain-of-thought reasoning in real-world applications
Use cases for chain-of-thought reasoning in real-world applications
LLM Fine-tuning and Customization:
Introduction to fine-tuning LLMs with LangChain
Leveraging pre-trained models for specific tasks (e.g., domain-specific applications)
Leveraging pre-trained models for specific tasks (e.g., domain-specific applications)
Handling Edge Cases:
Managing edge cases, failure modes, and improving application reliability
Module 8: Deploying and Scaling LLM-Powered Applications
Deploying LangChain Applications:
Best practices for deploying LLM-based applications on cloud platforms
CI/CD pipelines for deploying LangChain applications
CI/CD pipelines for deploying LangChain applications
Scaling LLM Applications:
Handling large-scale requests and ensuring high availability
Optimizing performance with caching and load balancing
Optimizing performance with caching and load balancing
Hands-on Project:
Deploying an LLM-powered web application on AWS/Azure/Heroku
Career Path After Completion:
Upon completing this course, learners will be equipped to pursue careers in the following areas:
AI Application Developer:Design and develop AI-driven applications using frameworks like LangChain.
Conversational AI Engineer:
Build advanced chatbots and virtual assistants with LLMs
.
Automation Engineer:
Develop automation workflows using LLM-powered tools.
Automation Engineer:
Develop automation workflows using LLM-powered tools.
AI Solutions Architect:
Design large-scale AI solutions that integrate LLMs into business processes.
Course Prerequisites:
Familiarity with programming concepts (prior experience with Python is recommended)Interest in learning AI-related technologies
International Student Fees: USD525$
Job Interview Preparation (Soft Skills Questions & Answers)
Tough Open-Ended Job Interview QuestionsWhat to Wear for Best Job Interview Attire
Job Interview Question- What are You Passionate About?
How to Prepare for a Job Promotion Interview
Stay connected even when you’re apart
Join our WhatsApp Channel – Get discount offers
500+ Free Certification Exam Practice Question and Answers
Your FREE eLEARNING Courses (Click Here)
Internships, Freelance and Full-Time Work opportunities
Join Internships and Referral Program (click for details)
Work as Freelancer or Full-Time Employee (click for details)
Flexible Class Options
Week End Classes For Professionals SAT | SUNCorporate Group Training Available
Online Classes – Live Virtual Class (L.V.C), Online Training
Related Courses
Computer Science for Artificial Intelligence Professional Certificate
AI For Everyone: A non-technical introduction to AI
Using A.I. Tools with Business Use Cases Practical Training
Diploma Artificial Intelligence
Introduction to Artificial Intelligence- AI for beginners
Artificial Intelligence (AI) Master Course
0 Reviews