1. Understand the architecture and philosophy of LangChain

2. Build prompt chains and control the flow of multi-step reasoning

3. Use memory to enable contextual awareness in LLM apps

4. Load and process documents for question-answering tasks

5. Connect LangChain to external tools like APIs, databases, and file systems

6. Develop intelligent agents with tool use and decision-making logic

7. Integrate vector stores for semantic search and RAG

8. Build chat interfaces using LangChain with Streamlit or FastAPI

9. Handle logging, debugging, and evaluation with LangSmith and LangServe

10. Deploy LangChain pipelines in production with version control and modularity

Course Syllabus

1. Introduction to LangChain and LLM-Driven Apps

2. LangChain Architecture: Chains, Agents, Tools, and Memory

3. Building Prompt Chains for Step-by-Step Reasoning

4. Memory Management: ConversationBuffer, Entity, and Vector Memory

5. Document Loaders and Chunking for QA Systems

6. Tools and Toolkits: Google Search, Bash, Python REPL, etc.

7. Using Agents for Decision-Based Reasoning and Action

8. Connecting to Vector Stores: Pinecone, FAISS, ChromaDB

9. Integrating LangChain with APIs and SQL Databases

10. Chatbot and RAG Applications Using LangChain

11. UI Integration with Streamlit and FastAPI

12. Monitoring, Logging, and Debugging with LangSmith

13. Case Study: Building a Research Assistant with LangChain

14. Deploying LangChain Pipelines in Production

Upon completion, learners will receive a Uplatz Certificate of Completion in LangChain Development. This credential validates your skills in developing complex LLM-powered workflows using LangChain’s modular framework. Employers will recognize your proficiency in chaining prompts, managing memory, integrating APIs, and deploying AI agents in production environments.

The certification demonstrates your ability to build scalable, intelligent applications that combine data retrieval, reasoning, and real-world interactivity—paving the way for careers in AI development, NLP engineering, and automation.

LangChain developers are in high demand as enterprises move toward intelligent, AI-powered systems. Knowing how to chain prompts, manage LLM context, and integrate third-party tools is a rare and valuable skill set.

Career paths include:

• LLM Application Developer

• Conversational AI Engineer

• AI Automation Specialist

• NLP Engineer

• Research Assistant Developer

• AI Solutions Architect

Industries such as healthcare, fintech, legal tech, and SaaS companies actively seek talent capable of building retrieval-augmented generation (RAG) apps, smart assistants, and LLM-backed dashboards. LangChain proficiency opens doors to top-paying roles in AI development and MLOps.

1. What is LangChain used for?
   LangChain is used to create LLM-powered applications like chatbots, research agents, and question-answering systems.

2. What are Chains in LangChain?
   Chains define a sequence of LLM prompts and actions that build upon each other to accomplish complex reasoning.

3. How does memory help in LangChain?
   Memory stores past interactions or context, enabling the LLM to maintain continuity in responses.

4. What are LangChain Agents?
   Agents choose actions (e.g., using tools or calling APIs) based on LLM outputs and user queries.

5. What’s the role of vector stores in LangChain?
   They allow semantic document search, which powers RAG and contextual responses.

6. Can LangChain integrate with APIs?
   Yes, LangChain supports external toolkits for web search, SQL queries, APIs, etc.

7. How is LangChain different from plain LLM APIs?
   LangChain adds chaining, memory, tool use, and flow control over basic prompt completions.

8. What is LangSmith used for?
   LangSmith helps monitor, debug, and evaluate LangChain applications.

9. Can LangChain apps run in production?
   Yes, LangChain is modular and supports production deployment with LangServe.

10. What is an example use case of LangChain?
    A research assistant that reads PDFs, searches online, and answers contextually is a common use case.