Coursera - Llm Application Engineering And Development Certification Specialization
 

Design and Development of LLM Applications
What you'll learn
Build GenAI workflows using LangChain, chains, agents, memory, and LLMs like Flan-T5 and Falcon-7B
Fine-tune LLMs with PEFT, RLHF, and hyperparameter tuning for real-world GenAI applications
Process unstructured data using loaders, text splitters, embeddings, and semantic search tools
Evaluate model output using benchmarks like ROUGE, GLUE, and BIG-bench for reliable performance
Skills you'll gain
Semantic Web
Tensorflow
Artificial Intelligence
Generative AI Agents
Agentic systems
Unstructured Data
ChatGPT
Generative AI
Benchmarking
Computer Programming Tools
PyTorch (Machine Learning Library)
Text Mining
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.
Applied Learning Project
Project Overview: Arxiv Paper Summarizer Using LangChain
In this project, you'll create a summarization tool that downloads a research paper from Arxiv, extracts its content, and generates a summary using LangChain. Starting with essential library imports, you'll download the paper PDF, extract the full text, and count the tokens to ensure compatibility with language models. Finally, you'll use LangChain to produce a concise and coherent summary. This project builds practical skills in PDF handling, text extraction, token analysis, and applying language models and ideal for learners interested in automating research workflows or summarizing academic content efficiently