This project is a comprehensive guide and framework for designing, optimizing, and securing inputs to improve the accuracy and reasoning of large language model outputs. It provides core methodologies for implementing logical reasoning steps, example-based learning, and reusable template systems.
The framework distinguishes itself through a focus on security guardrails and ethical auditing, implementing primitives to prevent adversarial prompt injection attacks and identify biases. It also emphasizes structured generation, using persona assignment and negative constraints to control the tone, expertise, and boundaries of generated text.
The project covers a broad range of capabilities including performance optimization via chain-of-thought and few-shot learning, as well as workflow management through sequential prompt chaining and context-window chunking. It further addresses the architectural needs of input standardization and output shaping to ensure consistency across different use cases.
The content is delivered primarily through Jupyter Notebooks.
