Large Language Models for Developers

Edureka via Coursera Specialization

Go to class Write review

Overview

Google, IBM & Meta Certificates – 40% Off

One Coursera Plus subscription covers most Professional Certificates on Coursera.

Unlock All Certificates

Large language models (LLMs) now power search, customer support, coding assistants, and enterprise knowledge tools, and building on them reliably has become a core developer skill. This Specialization takes you from LLM fundamentals and prompt engineering to tool-calling applications, retrieval-augmented generation (RAG), fine-tuning, and LLMOps. Every course is built around follow-along demos in Python, so you write and run working code as you learn. By the end of this Specialization, you will be able to: Explain how transformers, tokenization, and attention shape LLM behavior. Design structured, reasoning, and injection-resistant prompts for real tasks. Build LLM applications with provider APIs, tool calling, LangChain, and LlamaIndex. Implement RAG pipelines using embeddings and vector databases. Evaluate retrieval quality and fine-tune models with LoRA and QLoRA. Deploy and monitor GenAI services with FastAPI, Docker, and LLMOps practices. This Specialization suits software developers, Python programmers, data scientists, and ML engineers who want to add generative AI features to their products. Working knowledge of Python and APIs will help you get started, and no prior machine learning experience is required. Enroll now to start building LLM applications that are grounded, measurable, and ready to scale.

Syllabus

Course 1: Getting Started with LLMs and Prompt Design
Course 2: Building LLM Applications with Tools and Retrieval
Course 3: Evaluating and Scaling LLMs

Courses

0 reviews

View details

Building an LLM application requires more than a single prompt: it means calling model APIs, controlling output, connecting tools, and grounding answers in your own data. This course shows you how to build LLM applications in Python that use tool calling and retrieval-augmented generation (RAG). Through follow-along demos, you will call multiple LLM providers from one codebase, compose chains in LangChain, index documents with LlamaIndex, and store embeddings in ChromaDB, Pinecone, and Weaviate. By the end of this course, you will be able to: • Integrate LLM provider APIs and estimate token cost and latency. • Control output with generation parameters, streaming, and JSON schemas. • Implement tool calling to connect Python functions to a model. • Build chains, memory, and agents with LangChain and LlamaIndex. • Store and search embeddings using vector databases and ANN indexes. • Develop a RAG pipeline with chunking, retrievers, and cited answers. This course is designed for software developers, backend engineers, Python programmers, and AI engineers who want to turn LLM prototypes into working applications. You should be comfortable with Python and basic prompt engineering before you begin. Enroll now to build tool-using, retrieval-backed LLM applications.
0 reviews

View details

Moving a large language model (LLM) application from prototype to production requires measurable quality, the right model for each task, and reliable operations at scale. This course covers how to evaluate, optimize, and scale LLM systems. Through follow-along demos, you will improve retrieval with re-ranking and hybrid search, score RAG pipelines with RAGAS, fine-tune open models with LoRA and QLoRA on Hugging Face, and deploy a containerized GenAI API with FastAPI and Docker. By the end of this course, you will be able to: • Evaluate RAG pipelines using ranking metrics and RAGAS faithfulness scores. • Improve retrieval with re-ranking, HyDE, hybrid search, and advanced retrievers. • Select models by cost, latency, and quality, and fine-tune them with LoRA. • Build multimodal features with vision-language and speech models. • Deploy GenAI services with FastAPI, Docker, caching, and provider fallbacks. • Operate LLM systems with guardrails, red-teaming, tracing, and drift detection. This course is designed for AI engineers, ML engineers, backend developers, and MLOps practitioners responsible for shipping LLM features. You should be comfortable building LLM applications with Python, APIs, and RAG before you begin. Enroll now to evaluate, fine-tune, and scale LLM systems with confidence.
0 reviews

View details

Large language models (LLMs) and prompt design form the foundation of every generative AI application. This course explains how LLMs work and how to write prompts that produce accurate, consistent, and safe output. Through follow-along demos in Python, you will generate text with pretrained models, explore tokenization and attention, and apply prompting techniques to question answering, summarization, and classification tasks. By the end of this course, you will be able to: • Explain how transformers, tokens, embeddings, and attention drive LLM output. • Compare proprietary and open-source LLMs and assess responsible AI risks. • Design prompts using zero-shot, few-shot, and structured output techniques. • Apply chain-of-thought, tree-of-thought, and self-consistency prompting. • Defend prompted applications against prompt injection with human review. • Engineer context and manage token budgets for reliable responses. This course is designed for software developers, Python programmers, data analysts, and technical professionals beginning to build with generative AI. Basic Python skills will help you follow the demos, and no prior machine learning experience is required. Enroll now to build a working foundation in LLMs and prompt engineering.