AWS Machine Learning Blog

NOVEMBER 15, 2024

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

Responsible AI 105

Responsible AI Prompt Engineer Prompt Engineering AI 105

Snorkel AI

MARCH 4, 2025

Alignment ensures that an AI models outputs align with specific values, principles, or goals, such as generating polite, safe, and accurate responses or adhering to a company’s ethical guidelines. LLM alignment techniques come in three major varieties: Prompt engineering that explicitly tells the model how to behave.

LLM 52

LLM Large Language Models Data Quality Prompt Engineer 52

AWS Machine Learning Blog

JANUARY 28, 2025

Furthermore, evaluation processes are important not only for LLMs, but are becoming essential for assessing prompt template quality, input data quality, and ultimately, the entire application stack. It consists of three main components: Data config Specifies the dataset location and its structure.

LLM 95

LLM Large Language Models ML Algorithm 95

AWS Machine Learning Blog

NOVEMBER 7, 2024

Prompt catalog – Crafting effective prompts is important for guiding large language models (LLMs) to generate the desired outputs. Prompt engineering is typically an iterative process, and teams experiment with different techniques and prompt structures until they reach their target outcomes.

Generative AI 106

Generative AI Machine Learning AI AI 106

Snorkel AI

MARCH 4, 2025

Alignment ensures that an AI models outputs align with specific values, principles, or goals, such as generating polite, safe, and accurate responses or adhering to a company’s ethical guidelines. LLM alignment techniques come in three major varieties: Prompt engineering that explicitly tells the model how to behave.

LLM 59

LLM Large Language Models Data Quality Prompt Engineer 59

ODSC - Open Data Science

FEBRUARY 11, 2025

Building Real-World Applications: Lessons andMistakes Chip Huyen candidly shared common mistakes she has observed in AI application development: Overengineering: Many teams rush to use generative AI for tasks that simpler methods, such as decision trees, could handle more effectively. Focus on data quality over quantity.

AI Engineer 52

AI Engineer Machine Learning Software Engineer Prompt Engineer 52

O'Reilly Media

NOVEMBER 28, 2023

People with AI skills have always been hard to find and are often expensive. While experienced AI developers are starting to leave powerhouses like Google, OpenAI, Meta, and Microsoft, not enough are leaving to meet demand—and most of them will probably gravitate to startups rather than adding to the AI talent within established companies.

Generative AI 137

Generative AI AI AI Data Analysis 137