Sharpen Your AI Toolkit - Memory, Structured Output and Tools
Large Language Models (LLMs) are powerful on their own, but their true potential emerges when enhanced with additional capabilities. In their blog post “Building Effective Agents” 1, Anthropic describes these enhancements as “The augmented LLM” - a system that combines language models with memory, structured output, and tools. These capabilities transform basic text generators into practical problem-solving systems that can remember context, produce consistent data formats, and interact with the outside world.
This tutorial will teach you how to implement these fundamental building blocks for creating effective AI applications. Whether you’re building a chatbot, a knowledge assistant, or an automation system, these skills will help you move beyond simple prompting to create truly useful AI systems. Of course, we’ll do everything locally using Ollama (and Qwen 2.5).
Tutorial Goals
- Implement a memory system to maintain context in conversations
- Structure LLM outputs into predictable formats using Pydantic
- Enable LLMs to perform actions by connecting them to tools
- Use retrieval to give LLMs access to external knowledge sources