Definition Explainability Prompt Engineering 
AWS Machine Learning Blog
JANUARY 31, 2025
However, there are benefits to building an FM-based classifier using an API service such as Amazon Bedrock, such as the speed to develop the system, the ability to switch between models, rapid experimentation for prompt engineering iterations, and the extensibility into other related classification tasks.
Unite.AI
APRIL 3, 2025
Rather than debating abstract definitions of an “agent,” let's focus on practical implementation challenges and the capability spectrum that development teams are navigating today. This explains why 53.5% of teams rely on prompt engineering rather than fine-tuning (32.5%) to guide model outputs.
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AI News
MARCH 7, 2024
Indeed, as Anthropic prompt engineer Alex Albert pointed out, during the testing phase of Claude 3 Opus, the most potent LLM (large language model) variant, the model exhibited signs of awareness that it was being evaluated. The company says it has also achieved ‘near human’ proficiency in various tasks.
AWS Machine Learning Blog
FEBRUARY 19, 2025
Though these models can produce sophisticated outputs through the interplay of pre-training, fine-tuning , and prompt engineering , their decision-making process remains less transparent than classical predictive approaches. FMs are probabilistic in nature and produce a range of outcomes.
AWS Machine Learning Blog
DECEMBER 23, 2024
In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria. At query time, user intent is turned into an efficient graph query based on domain definition to retrieve the relevant entities and relationship.
AWS Machine Learning Blog
MARCH 27, 2025
In the following sections, we explain how to take an incremental and measured approach to improve Anthropics Claude 3.5 Sonnet prediction accuracy through prompt engineering. We suggest consulting LLM prompt engineering documentation such as Anthropic prompt engineering for experiments.
AWS Machine Learning Blog
NOVEMBER 15, 2024
For now, we consider eight key dimensions of responsible AI: Fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. You define a denied topic by providing a natural language definition of the topic along with a few optional example phrases of the topic.
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