Building LLM Powered Applications

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Building LLM Powered Applications

Coursera · Intermediate ·🧠 Large Language Models ·3mo ago

Key Takeaways

Provides a comprehensive introduction to building intelligent applications powered by large language models

Original Description

This course provides a comprehensive introduction to building intelligent applications powered by large language models (LLMs). You'll explore foundational LLM concepts, architectural frameworks, and practical applications in real-world scenarios. By using leading LLM toolkits and frameworks, you'll gain hands-on experience in creating intelligent agents capable of handling both structured and unstructured data. The course emphasizes the integration of LangChain for orchestrating complex AI workflows and covers prompt engineering techniques essential for customizing and optimizing LLMs. What sets this course apart is its blend of theoretical learning and practical implementation, making it an ideal resource for those looking to implement LLMs in real-world applications. It ensures you can build LLM-powered applications from scratch while navigating the challenges of real-world scenarios, including ethical considerations. This course is suitable for software engineers, data scientists, and researchers who are keen on understanding the applied aspects of generative AI. No prior experience with LLMs is required, but a strong understanding of machine learning concepts will enhance your learning experience. Based on the book, Building LLM Powered Applications, by Valentina Alto.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
The MMM Data Model -- A Normative Specification for Knowledge Interoperability in a Decentralisable Knowledge Commons
Learn about the MMM Data Model for knowledge interoperability in decentralised systems and how it enables flexible knowledge structuring and sharing
ArXiv cs.AI
📰
Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming
Learn to detect epistemic aims and processes in student-AI co-programming to improve AI literacy, crucial for effective learning with generative AI
ArXiv cs.AI
📰
From Signals to Structure: How Memory Architecture Drives Language Emergence in LLM Agents
Discover how memory architecture impacts language emergence in LLM agents and learn to design effective memory systems for agent communication
ArXiv cs.AI
📰
Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity
Learn about Seed2.0, a model series tackling real-world complexity by identifying user needs and constructing a reliable evaluation system
ArXiv cs.AI
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →