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Episode 2: Behind the Curtain – How Language Models Think and See the World
Dive deep into the engine room of Large Language Models. We explore how LLMs process information, the differences compared to human thinking, and phenomena like "hallucinations." Learn about tokenization, auto-regressive models, the Transformer architecture, and how settings like "temperature" affect the output.
The DevOps Blueprint: Principles and Business Impact
"Explores the foundational Three Ways framework, business value of DevOps, and how to measure technical outcomes like lead time and MTTR. Includes real-world case studies on flow optimization."
Episode 1: The Spark – Taming the Magic of Language Models
Join us on a journey back to the dawn of language models. We explore how early models paved the way for revolutionary technologies like GPT, and introduce the basics of prompt engineering – the art and science of communicating effectively with these powerful AI tools. Understand how we got here and why prompt engineering is the key to unlocking the full potential of LLMs.
DevOps Handbook Key Concepts and Practices
The Three Ways of DevOps: While not explicitly detailed in these excerpts, the underlying principles of Flow, Feedback, and Continual Experimentation and Learning are evident throughout the practices described. Optimizing the Value Stream: Identifying and eliminating waste, reducing batch sizes, and decreasing lead times are central to improving the flow of work from idea to production. Culture of Continual Experimentation and Learning: Fostering a safe environment for learning from both successes and failures, and reserving time for improvement work, are crucial for driving organizational learning and resilience. Embedding Security into the Daily Work: Integrating information security practices and responsibilities throughout the value stream, rather than treating it as a separate function, is essential for building secure systems. Leveraging Telemetry and Data for Informed Decision Making: Using comprehensive monitoring and metrics at all levels of the application stack is vital for understanding system behavior, detecting anomalies, and driving improvement.