AWS Machine Learning Blog

JUNE 25, 2024

With Amazon Bedrock, developers can experiment, evaluate, and deploy generative AI applications without worrying about infrastructure management. Its enterprise-grade security, privacy controls, and responsible AI features enable secure and trustworthy generative AI innovation at scale.

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Automation Prompt Engineering Prompt Engineer Categorization 123

Unite.AI

JANUARY 23, 2024

Prompting Rather than inputs and outputs, LLMs are controlled via prompts – contextual instructions that frame a task. Prompt engineering is crucial to steering LLMs effectively. Hybrid retrieval combines dense embeddings and sparse keyword metadata for improved recall.

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Large Language Models Prompt Engineering Prompt Engineer LLM 100

The MLOps Blog

JUNE 27, 2023

The platform also offers features for hyperparameter optimization, automating model training workflows, model management, prompt engineering, and no-code ML app development. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support.

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Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

NOVEMBER 7, 2024

In this second part, we expand the solution and show to further accelerate innovation by centralizing common Generative AI components. We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. They’re illustrated in the following figure.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

You can use metadata filtering to narrow down search results by specifying inclusion and exclusion criteria. Responsible AI Implementing responsible AI practices is crucial for maintaining ethical and safe deployment of RAG systems. You can use Amazon Bedrock Guardrails for implementing responsible AI policies.

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Auto-classification Metadata Generative AI Machine Learning 92

Mlearning.ai

JULY 8, 2023

Add Responsible AI to LLM’s Add Abuse detection to LLM’s. Prompt Engineering — this is where figuring out what is the right prompt to use for the problem. Add monitoring and auditing code to log prompts and completion. Introduction Create ML Ops for LLM’s Build end to end development and deployment cycle.

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Large Language Models LLM Prompt Engineer Prompt Engineering 52

AWS Machine Learning Blog

AUGUST 21, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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Generative AI Prompt Engineering Prompt Engineer Automation 124

AWS Machine Learning Blog

JANUARY 28, 2025

By investing in robust evaluation practices, companies can maximize the benefits of LLMs while maintaining responsible AI implementation and minimizing potential drawbacks. To support robust generative AI application development, its essential to keep track of models, prompt templates, and datasets used throughout the process.

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LLM Large Language Models ML Algorithm 110

AWS Machine Learning Blog

JUNE 20, 2024

Additionally, evaluation can identify potential biases, hallucinations, inconsistencies, or factual errors that may arise from the integration of external sources or from sub-optimal prompt engineering. In this case, the model choice needs to be revisited or further prompt engineering needs to be done.

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Auto-classification LLM Prompt Engineering Prompt Engineer 135

Snorkel AI

MARCH 2, 2023

The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration. This goes into the model risk management in analyzing the metadata around the model, whether it’s fit for purpose with automated or human-in-the-loop capabilities.

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Large Language Models Prompt Engineering Prompt Engineer Neural Network 52

Snorkel AI

MARCH 2, 2023

The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration. This goes into the model risk management in analyzing the metadata around the model, whether it’s fit for purpose with automated or human-in-the-loop capabilities.

Large Language Models 52

Large Language Models Prompt Engineering Prompt Engineer Neural Network 52

IBM Journey to AI blog

OCTOBER 30, 2024

The enhanced metadata supports the matching categories to internal controls and other relevant policy and governance datasets. The platform incorporates the innovative Prompt Lab tool, specifically engineered to streamline prompt engineering processes.

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Prompt Engineering Prompt Engineer ETL Machine Learning 238

AWS Machine Learning Blog

NOVEMBER 25, 2024

This prompted 123RF to search for a more reliable and affordable solution to enhance multilingual content discovery. This post explores how 123RF used Amazon Bedrock, Anthropic’s Claude 3 Haiku, and a vector store to efficiently translate content metadata, significantly reduce costs, and improve their global content discovery capabilities.

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Prompt Engineering Prompt Engineer Metadata Generative AI 100

AWS Machine Learning Blog

NOVEMBER 29, 2023

An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Furthermore, evaluating LLMs can also help mitigating security risks, particularly in the context of prompt data tampering. The following diagram illustrates this architecture.

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LLM Data Scientist Algorithm Generative AI 114

AWS Machine Learning Blog

FEBRUARY 3, 2025

By using a combination of transcript preprocessing, prompt engineering, and structured LLM output, we enable the user experience shown in the following screenshot, which demonstrates the conversion of LLM-generated timestamp citations into clickable buttons (shown underlined in red) that navigate to the correct portion of the source video.

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UX Design Auto-complete LLM Prompt Engineering 105