What will you learn in Introduction to Large Language Models Course
Understand what Large Language Models (LLMs) are and how they function in AI systems.
Identify practical use cases for LLMs across industries and tasks.
Learn prompt tuning strategies to guide LLM outputs effectively.
Explore Google’s generative AI development tools and platform.
Program Overview
Section 1: Introduction to Large Language Models
⏳ 154 minutes
Topics: Define Large Language Models, explore use cases, explain prompt tuning, and introduce tools like Model Garden and Generative AI Studio.
Hands-on: Practice prompt tuning techniques and explore Gen AI tools through interactive assessments and a short hands-on assignment.
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Job Outlook
High global demand for AI-literate professionals and prompt engineers.
Knowledge of LLMs and GenAI tools is increasingly valuable in roles like data analysts, AI product managers, and ML engineers.
Entry-level roles may start at $60K–$90K, while GenAI consultants and ML specialists can earn over $120K.
Prompt engineering, LLM optimization, and generative design are emerging skillsets for freelancers and startups.
Explore More Learning Paths
Deepen your understanding of Large Language Models (LLMs) and AI-driven applications with these hand-picked courses designed to help you build, deploy, and leverage LLMs effectively.
Related Courses
Generative AI Engineering with LLMs Specialization Course – Learn to design, implement, and optimize LLM-powered applications and AI systems.
Introduction to Large Language Models Course – Explore LLM fundamentals, architectures, and practical use cases in real-world scenarios.
Guide to Building Python and LLM-based Multimodal Chatbots Course – Gain hands-on experience creating chatbots that integrate LLMs and Python for advanced conversational AI.
Related Reading
What Is Data Management? – Understand how proper data management supports LLM performance, AI training, and application deployment.
Specification: Introduction to Large Language Models Course
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FAQs
- No prior ML or AI experience is required.
- Basic understanding of Python or programming logic is helpful but optional.
- Introduces LLM concepts gradually for beginners.
- Hands-on exercises focus on real-world applications rather than coding.
- Ideal for anyone interested in GenAI or AI tools.
- The course does not cover building or training LLMs from scratch.
- Focuses on prompt tuning and guiding model outputs.
- Introduces tools like Google’s Model Garden and Generative AI Studio.
- Ideal for learning practical application in various domains.
- Serves as a foundation before tackling advanced AI courses.
- Teaches prompt tuning for effective outputs in LLMs.
- Provides hands-on exercises with real AI tools.
- Applicable in domains like data analysis, marketing, and content generation.
- Helps understand how LLMs integrate into workflows.
- Prepares learners for beginner-level AI and prompt engineering tasks.
- No deep coding is required.
- Focuses on conceptual understanding and practical tool usage.
- Hands-on assignments are guided and interactive.
- Python basics may help but are not mandatory.
- Suitable for non-technical professionals exploring AI applications.
- Prepares for roles like AI product assistant, prompt engineer, or data analyst.
- Supports freelance opportunities in content generation or AI consulting.
- Useful for positions involving AI tool integration.
- Introduces emerging skillsets in generative AI.
- Knowledge can be applied in startups or innovation-driven projects.
