Ever felt like your large language model applications hit a wall when faced with complex tasks? This podcast dives into how we can move beyond single prompt-response interactions and build truly agentic systems that can tackle intricate challenges like trip planning or code refactoring.

We introduce the idea of an augmented LLM as the fundamental building block. Think of your standard LLM but with added 'superpowers' like the ability to retrieve information, use external tools, and remember context. These augmentations are key to creating more capable AI.

We’ll explores five core design patterns that leverage these augmented LLMs to construct robust workflows . These patterns are:

* Routing

* Prompt Chaining

* Parallelization

* Orchestrator-Workers

* Evaluator-Optimizer

We’ll also discuss a practical example of this, a Coding Agent, inspired by GitHub Copilot.

We’ll cover a valuable overview of the fundamental concepts and design patterns necessary to build sophisticated AI applications that can handle much more than simple questions.

It's about moving towards LLM-powered agents that can truly take action and manage complex processes.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit galacticnaut.substack.com