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About This Course
- LangChain in Action: Develop LLM-Powered Applications Course Overview
The LangChain in Action course introduces learners to building intelligent applications using large language models (LLMs) and modern AI development frameworks. It explains how LangChain is used to connect language models with external data sources, APIs, and tools to create real-world AI-powered solutions. The course is designed to help learners move from basic AI concepts to practical application development.
The programme covers core components such as chains, agents, memory systems, and prompt engineering. Learners explore how to build workflows that allow LLMs to process information, retrieve data, and generate meaningful outputs. It also introduces integration with APIs, vector databases, and retrieval-augmented generation (RAG) systems used in advanced AI applications.
By completing this course, learners gain practical experience in designing and deploying AI-powered applications. It strengthens programming logic, AI system design, and problem-solving skills. The course prepares individuals to work on modern AI development projects and build scalable LLM-based applications for real-world use cases.
- What You Will Learn from LangChain Course
- Understand LangChain framework and architecture clearly
- Build LLM-powered applications using chains effectively
- Work with agents and memory systems properly
- Apply prompt engineering for better AI responses
- Integrate APIs with language model workflows
- Use vector databases for information retrieval systems
- Develop retrieval-augmented generation (RAG) applications
- Design scalable AI-driven application workflows
- Why Should You Choose this LangChain Course?
This course provides hands-on knowledge of building AI applications using modern LLM frameworks. It focuses on practical development, allowing learners to understand how artificial intelligence systems are designed, connected, and deployed in real-world environments.
It also supports career growth in artificial intelligence and software development fields. The training builds technical confidence, problem-solving ability, and understanding of AI system architecture, making it ideal for learners aiming to enter the fast-growing AI industry.
- Key Topics Covered
- Introduction to LangChain framework and components
- Memory systems in AI applications explained
- Chains, prompts, and LLM interaction workflows
- Agents and decision-making AI models
- API integration with LLM-based systems
- Vector databases and semantic search concepts
- Retrieval-augmented generation (RAG) systems
- Deployment of AI applications in real environments
- Who Should Take This Course?
This course is suitable for aspiring AI developers, software engineers, data scientists, and tech enthusiasts interested in building LLM-powered applications. It is ideal for individuals with basic programming knowledge who want to enter the field of artificial intelligence development.
It also benefits professionals looking to upgrade their skills in AI integration and modern application development. The training provides a strong foundation for building intelligent systems using cutting-edge AI frameworks and tools.
- Entry Requirements
There are no strict academic requirements for this course. However, basic understanding of programming (preferably Python), logical thinking, andaccess to a computer with internet connection are recommended. It is suitable for beginners in AI development.
- Why Should You Learn with Imperial Academy
- Offers high-quality, industry-focused online courses
- Builds practical, job-ready skills across multiple sectors
- Provides flexible learning accessible anytime, anywhere
- Supports learners with structured and easy-to-follow content
- Enhances career opportunities and professional development
- Improves confidence through guided learning experience
- Career Prospects
- AI Application Developer (£40,000–£80,000 annually)
- Machine Learning Engineer (£45,000–£90,000 annually)
- NLP Engineer (£42,000–£85,000 annually)
- AI Solutions Developer (£40,000–£75,000 annually)
- Software Engineer (AI Focus) (£38,000–£80,000 annually)
- Data Scientist (£45,000–£95,000 annually)
Course Curriculum
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Why this course is different
00:01:00 -
Prerequisites
00:01:00
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Essential topics and terms (theory)
00:04:00
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Why this course does not cover Open Source models like LLama2
00:01:00
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Optional: Install Visual Studio Code
00:02:00
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Get the source files with Git from Github
00:02:00
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Create OpenAI Account and create API Key
00:02:00
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Setup of a virtual environment
00:03:00
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Setup OpenAI Api-Key as environment variable
00:03:00
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Exploring the vanilla OpenAI package
00:03:00
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LLM Basics
00:07:00
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Prompting Basics
00:02:00
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Theory: Prompt Engineering Basics
00:02:00
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Few Shot Prompting
00:05:00
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Chain of thought prompting
00:02:00
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Pipeline-Prompts
00:04:00
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Prompt Serialisation
00:03:00
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Introduction to chains
00:01:00
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Basic chains – the LLMChain
00:03:00
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Response Schemas and OutputParsers
00:06:00
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LLMChain with multiple inputs
00:02:00
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SequentialChains
00:04:00
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RouterChains
00:04:00
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Callbacks
00:05:00
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Memory basics – ConversationBufferMemory
00:04:00
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Conversation Summary Memory
00:03:00
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EXERCISE: Use Memory to build a streamlit Chatbot
00:01:00
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SOLUTION: Chatbot with Streamlit
00:03:00
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OpenAI Function Calling – Vanilla OpenAI Package
00:08:00
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Function Calling with LangChain
00:04:00
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Limits and issues of the langchain Implementation
00:03:00
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RAG – Theory and building blocks
00:03:00
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Loaders and Splitters
00:04:00
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Embeddings – Theory and practice
00:04:00
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VectorStores and Retrievers
00:07:00
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RAG Service with FastAPI
00:05:00
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Agents Basics – LLMs learn to use tools
00:06:00
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Agents with a custom RAG-Tool
00:08:00
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ChatAgents
00:03:00
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Indexing API – keep your documents in sync
00:02:00
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PREREQUISITE: Docker Installation
00:01:00
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Setup of PgVector and RecordManager
00:04:00
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Indexing Documents in practice
00:06:00
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Document Retrieval with PgVector
00:03:00
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Introduction to LangSmith (User Interface and Hub)
00:02:00
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LangSmith Projects
00:07:00
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LangSmith Datasets and Evaluation
00:13:00
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Introduction to Microservice Architecture
00:04:00
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How our Chatbot works in a Microservice Architecture
00:02:00
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Introduction to Docker
00:05:00
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Introduction to Kubernetes
00:02:00
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Deployment of the LLM Microservices to Kubernetes
00:13:00
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Intro to LangChain Expression Language
00:01:00
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LCEL Part 1 – Pipes and OpenAI Function Calling
00:07:00
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LCEL – Part 2 – VectorStores, ItemGetter, Tools
00:06:00
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LCEL – Part 3 – Arbitrary Functions, Runnable Interface, Fallbacks
00:07:00
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