#generative-ai (18)
- Enhancing Agent Intelligence with Tools and Multi-Step Workflows
Equip your ADK agent with external tools and orchestrate complex, multi-step workflows, leveraging persistent state for robust, intelligent behavior.
- Build and Deploy Robust AI Systems for Production
Learn to build, deploy, and maintain robust, scalable AI systems, covering MLOps, LLMOps, and best practices for production-ready applications.
- AI Coding Assistants: Shifting Bottlenecks in Software Delivery
Learn why AI coding assistants don't accelerate software delivery and how to leverage AI to improve the entire SDLC, from coding to deployment.
- Prompt Engineering Fundamentals for Effective LLM Communication
Master fundamental prompt engineering techniques to effectively communicate with Large Language Models and build your first interactive AI applications.
- Mastering Production Prompt Engineering & Agentic AI
Master prompt engineering & agentic AI for developers. This 2026 guide focuses on real-world production workflows, taking you from beginner to mastery.
- Unveiling AI Agents: The Next Frontier in Application Development
Discover the foundational concepts of AI agents, their architecture, and why they represent a paradigm shift in building intelligent applications beyond simple LLM prompts.
- Introduction to AI Agent Memory: Why Agents Need to Remember
Explore the fundamental need for memory in AI agents, understanding how it overcomes LLM limitations and enables more intelligent, stateful, and personalized interactions.
- The Future Horizon: Emerging Trends and Challenges in AI DevOps
Explore the cutting-edge trends, emerging challenges, and critical considerations for the future of AI in DevOps, focusing on responsible innovation.
- Regression Testing for AI: Preventing Unintended Consequences
Discover how to implement robust regression testing strategies for AI systems to prevent unintended consequences, maintain performance, and ensure reliability in production.
- Foundations of AI System Evaluation: Metrics & Benchmarking
Explore the foundational concepts of AI system evaluation, including critical metrics for various AI tasks and robust benchmarking strategies to ensure reliability and performance.
- Architecting AI Systems with LLMs, Generative AI, and Agents
Learn to design and integrate scalable, trustworthy AI systems that leverage Large Language Models, Generative AI, and multi-agent orchestration patterns.
- Creating Diverse Content with Generative Multimodal AI
Grasp the core principles and architectures of generative multimodal AI to create novel content by integrating text, images, audio, and video inputs.
- Multimodal LLMs: How AI Interprets and Generates Across Modalities
Learn how Multimodal Large Language Models integrate diverse data and extend AI to interpret and generate content across modalities.
- Understanding Multimodal AI and Combining Data for Perception
You will learn why combining text, image, audio, and video inputs is crucial for creating more intelligent and human-like AI systems.
- Retrieval-Augmented Generation: Basics, Architecture, Limitations
Learn the core architecture and functionality of basic Retrieval-Augmented Generation and identify its key limitations for AI systems.
- Advanced RAG: LLM Query Rewriting and Multi-Hop Retrieval
Discover how LLMs enhance RAG retrieval, rewriting complex user queries and orchestrating multi-step searches to find more accurate information.
- Deploying RAG 2.0: Best Practices, Evaluation, and Real-World Projects
Explore best practices for deploying RAG 2.0 systems, learn crucial evaluation methodologies, and discover real-world applications to build robust and accurate Generative AI solutions.
- The Road Ahead: Future of AI & Career Paths
Explore the future of AI, ethical considerations, and diverse career paths for those interested in this rapidly evolving field.