LLM Application Engineering and Development Certification

Overview

This specialization offers a hands-on journey into building and deploying applications powered by Large Language Models (LLMs) and LangChain. Learn to design GenAI workflows using LangChain’s architecture—including chains, memory, agents, and prompts—and integrate advanced models like Flan-T5 XXL and Falcon-7B.

Process unstructured data, implement embeddings, and enable semantic retrieval for intelligent applications. Fine-tune LLMs using techniques like PEFT and RLHF, and evaluate performance using benchmarks such as ROUGE, GLUE, and BIG-bench to ensure model reliability.

By the end of this program, you will be able to:

- Design LLM Workflows:

Build scalable GenAI apps using LangChain with memory and agent modules - Process and Retrieve Data:

Use loaders, vector stores, and embeddings for semantic search - Fine-Tune and Customize Models:

Apply PEFT, RLHF, and dataset structuring for optimization - Evaluate and Scale Applications:

Use standard benchmarks and deploy industry-grade LLM tools Ideal for developers, data scientists, and GenAI enthusiasts building advanced, real-world LLM applications.

Syllabus

• Introduction to Large Language Models (LLMs)
Overview of LLMs: Capabilities and Use Cases
Understanding LangChain’s Architecture: Chains, Memory, Agents, Prompts
Exploration of Advanced Models: Flan-T5 XXL, Falcon-7B
• Designing LLM Workflows
Building Scalable GenAI Applications with LangChain
Implementing Memory and Agents in LLM Architectures
Designing Effective Prompting Strategies
• Data Processing and Retrieval
Handling Unstructured Data
Introduction to Data Loaders and Vector Stores
Implementing Embeddings for Semantic Search and Retrieval
• Fine-Tuning and Customizing LLMs
Introduction to Parameter-Efficient Fine-Tuning (PEFT)
Techniques for Reinforcement Learning with Human Feedback (RLHF)
Structuring Datasets for Optimal Model Training and Customization
• Evaluation and Deployment of LLM Applications
Using Benchmarks: ROUGE, GLUE, BIG-bench for Model Performance Evaluation
Strategies for Ensuring Model Reliability and Performance
Deploying Industry-Grade LLM Solutions
• Project Work: Building a Real-World LLM Application
Designing and Developing a Capstone Project using LangChain and LLMs
Integration of Advanced Techniques for Optimization and Performance
Presentation and Peer Review of Project Outcomes
• Conclusion and Certification
Review of Key Concepts and Techniques
Certification Assessment and Final Evaluation
• Additional Resources and Next Steps
Access to Further Learning Materials and Community Networks
Guidance on Career Pathways in LLM Application Engineering

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Taught by

Priyanka Mehta

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Subjects

Computer Science