AWS Machine Learning Blog

JANUARY 22, 2025

The key to the capability of the solution is the prompts we have engineered to instruct Anthropics Claude what to do. Prompt engineering Prompt engineering is the process of carefully designing the input prompts or instructions that are given to LLMs and other generative AI systems.

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Generative AI Prompt Engineering Prompt Engineer AI 99

AWS Machine Learning Blog

JANUARY 22, 2025

Users can review different types of events such as security, connectivity, system, and management, each categorized by specific criteria like threat protection, LAN monitoring, and firmware updates. For our specific task, weve found prompt engineering sufficient to achieve the results we needed.

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Prompt Engineering Prompt Engineer Natural Language Processing Machine Learning 100

Unite.AI

SEPTEMBER 20, 2023

To install and import the library, use the following commands: pip install -q transformers from transformers import pipeline Having done that, you can execute NLP tasks starting with sentiment analysis, which categorizes text into positive or negative sentiments.

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AWS Machine Learning Blog

JUNE 25, 2024

Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. Operational efficiency Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced.

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Automation Prompt Engineering Prompt Engineer Categorization 114

Marktechpost

MAY 7, 2024

Researchers have primarily focused on enhancing NL2SQL methods, which can be categorized into pre-trained and Supervised Fine-Tuning (SFT) methods, prompt engineering-based LLMs, and LLMs specifically trained for NL2SQL. The researchers propose novel solutions tailored to the NL2BI scenario to address these challenges.

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Business Intelligence Prompt Engineer Prompt Engineering Natural Language Processing 117

Marktechpost

AUGUST 31, 2024

This includes careful prompt engineering for the LLM and potential feedback loops where the LLM’s explanations can be used to refine the SLM’s detection criteria over time. The experimental results demonstrate the effectiveness of the proposed hallucination detection framework, particularly the Categorized approach.

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Large Language Models Categorization LLM Explainability 128

ODSC - Open Data Science

APRIL 15, 2025

Before you dive into prompt engineering or fine-tuning, its essential to master the AI basics and data science fundamentals. What youll do: This includes normalization, aggregation, encoding categorical variables, and converting temporal data into usable formats. But what are they?

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Data Science Prompt Engineering Prompt Engineer Categorization 52

Unite.AI

AUGUST 8, 2023

Information Retrieval: Using LLMs, such as BERT or GPT, as part of larger architectures to develop systems that can fetch and categorize information. This recent post demystifies Midjourney in a detailed guide, elucidating both the platform and its prompt engineering intricacies.

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Unite.AI

JANUARY 11, 2024

As the paper demonstrated, prompt engineering allows creating training data for hundreds of thousands of embedding tasks. Yet, current prompt design practices remain more an art than science. However, critical research directions remain to translate this potential into real-world impact.

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Large Language Models Prompt Engineering Prompt Engineer BERT 147

AWS Machine Learning Blog

NOVEMBER 1, 2024

Fine-tuning Anthropic’s Claude 3 Haiku has demonstrated superior performance compared to few-shot prompt engineering on base Anthropic’s Claude 3 Haiku, Anthropic’s Claude 3 Sonnet, and Anthropic’s Claude 3.5 Sonnet across various tasks.

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LLM Prompt Engineering Prompt Engineer Generative AI 87

AWS Machine Learning Blog

MAY 7, 2024

This post walks through examples of building information extraction use cases by combining LLMs with prompt engineering and frameworks such as LangChain. Prompt engineering Prompt engineering enables you to instruct LLMs to generate suggestions, explanations, or completions of text in an interactive way.

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Prompt Engineer Prompt Engineering Large Language Models LLM 110

Marktechpost

AUGUST 10, 2024

Software engineers have shown reluctance to use LLMs for higher-level design tasks due to concerns about complex requirement comprehension. Despite this, LLMs’ use in requirement engineering has gradually increased, driven by advancements in contextual analysis and reasoning through prompt engineering and Chain-of-Thought techniques.

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Software Engineer LLM Large Language Models Categorization 113

Marktechpost

MAY 11, 2024

IE tasks compel models to discern and categorize text in formats that align with predefined structures, such as named entity recognition and relation classification. However, existing LLMs typically falter when tasked with the nuanced understanding and alignment necessary for effective IE.

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Large Language Models Prompt Engineering Prompt Engineer Categorization 109

Artificial Corner

JULY 30, 2023

Machine translation, summarization, ticket categorization, and spell-checking are among the examples. Prompts design is a process of creating prompts which are the instructions and context that are given to Large Language Models to achieve the desired task. What are large language models used for?

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Large Language Models LLM Natural Language Processing Deep Learning 98

AWS Machine Learning Blog

OCTOBER 6, 2023

For instance, in ecommerce, image-to-text can automate product categorization based on images, enhancing search efficiency and accuracy. With millions of products listed, effective sorting and categorization poses a significant challenge. This is where the power of auto-tagging and attribute generation comes into its own.

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Generative AI Prompt Engineering Prompt Engineer Computer Vision 112

AWS Machine Learning Blog

SEPTEMBER 14, 2023

Document categorization or classification has significant benefits across business domains – Improved search and retrieval – By categorizing documents into relevant topics or categories, it makes it much easier for users to search and retrieve the documents they need. They can search within specific categories to narrow down results.

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Categorization Machine Learning Data Scientist Natural Language Processing 110

AWS Machine Learning Blog

NOVEMBER 7, 2024

Some components are categorized in groups based on the type of functionality they exhibit. Prompt catalog – Crafting effective prompts is important for guiding large language models (LLMs) to generate the desired outputs. Having a centralized prompt catalog is essential for storing, versioning, tracking, and sharing prompts.

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Unite.AI

AUGUST 28, 2023

link] The process can be categorized into three agents: Execution Agent : The heart of the system, this agent leverages OpenAI’s API for task processing. Given an objective and a task, it prompts OpenAI's API and retrieves task outcomes.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

Curated judge models : Amazon Bedrock provides pre-selected, high-quality evaluation models with optimized prompt engineering for accurate assessments. Users dont need to bring external judge models, because the Amazon Bedrock team maintains and updates a selection of judge models and associated evaluation judge prompts.

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LLM Generative AI Automation Machine Learning 86

Marktechpost

NOVEMBER 19, 2024

Current methods to limit these LLM vulnerabilities include adversarial testing, red-teaming exercises, and manual prompt engineering. The identified vulnerabilities are categorized based on their impact, severity, and potential exploitability, providing a structured approach to addressing risks.

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LLM Prompt Engineering Prompt Engineer Large Language Models 84

Unite.AI

JANUARY 19, 2024

Taxonomy of Hallucination Mitigation Techniques Researchers have introduced diverse techniques to combat hallucinations in LLMs, which can be categorized into: 1. Prompt Engineering This involves carefully crafting prompts to provide context and guide the LLM towards factual, grounded responses.

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Large Language Models LLM Prompt Engineering Prompt Engineer 113

Marktechpost

SEPTEMBER 15, 2024

These sources can be categorized into three types: textual documents (e.g., Techniques like Uprise and DaSLaM use lightweight retrievers or small models to optimize prompts, break down complex problems, or generate pseudo labels. KD methods can be categorized into white-box and black-box approaches.

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BERT LLM Large Language Models Categorization 120

AWS Machine Learning Blog

MARCH 13, 2024

Use LLM prompt engineering to accommodate customized policies The pre-trained Toxicity Detection models from Amazon Transcribe and Amazon Comprehend provide a broad toxicity taxonomy, commonly used by social platforms for moderating user-generated content in audio and text formats.

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Natural Language Processing LLM Prompt Engineering Prompt Engineer 122

Flipboard

DECEMBER 6, 2023

Types of summarizations There are several techniques to summarize text, which are broadly categorized into two main approaches: extractive and abstractive summarization. Given their versatile nature, these models require specific task instructions provided through input text, a practice referred to as prompt engineering.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

Operationalization journey per generative AI user type To simplify the description of the processes, we need to categorize the main generative AI user types, as shown in the following figure. Strong domain knowledge for tuning, including prompt engineering, is required as well. We will cover monitoring in a separate post.

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Generative AI Prompt Engineering Prompt Engineer ML 118

AWS Machine Learning Blog

OCTOBER 24, 2023

When a new document type introduced in the IDP pipeline needs classification, the LLM can process text and categorize the document given a set of classes. For example, we can follow prompt engineering best practices to fine-tune an LLM to format dates into MM/DD/YYYY format, which may be compatible with a database DATE column.

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IDP LLM Prompt Engineering Prompt Engineer 127

AWS Machine Learning Blog

AUGUST 22, 2024

Verisk’s evaluation involved three major parts: Prompt engineering – Prompt engineering is the process where you guide generative AI solutions to generate desired output. Verisk framed prompts using their in-house clinical experts’ knowledge on medical claims.

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Generative AI Auto-complete Software Development Automation 99

Unite.AI

AUGUST 4, 2023

In this article, we will delve deeper into these issues, exploring the advanced techniques of prompt engineering with Langchain, offering clear explanations, practical examples, and step-by-step instructions on how to implement them. Prompts play a crucial role in steering the behavior of a model.

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Prompt Engineering Prompt Engineer Python NLP 245

Towards AI

SEPTEMBER 26, 2023

OpenAI Announces DALL·E 3 OpenAI is launching DALL·E 3, an improved version that excels in following instructions, requires less prompt engineering, and can communicate with ChatGPT. This integration enables users to refine DALL·E 3 prompts by describing their ideas to ChatGPT. Five 5-minute reads/videos to keep you learning 1.Adept.ai

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Prompt Engineering Prompt Engineer OpenAI ChatGPT 64

Marktechpost

JANUARY 11, 2024

We have categorized them to make it easier to cover maximum tools. Built on ChatGPT, this version enhances user-friendliness by eliminating the need for complex prompt engineering. Operating based on natural language inputs or prompts, the model generates accurate images corresponding to the provided descriptions.

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Large Language Models Natural Language Processing Artificial Intelligence Artificial Intelligence 104

Bugra Akyildiz

AUGUST 27, 2023

LARs are a type of embedding that can be used to represent high-dimensional categorical data in a lower-dimensional continuous space. Prompt engineering aims to solve these problems, but comes with a steep learning curve and increased fragility as the prompt increases in size.

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OpenAI Large Language Models Categorization LLM 52

AWS Machine Learning Blog

MAY 1, 2024

It allows you to retrieve data from sources beyond the foundation model, enhancing prompts by integrating contextually relevant retrieved data. You can use prompt engineering to prevent hallucination and make sure that the answer is grounded in the source documentations.

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AWS Machine Learning Blog

APRIL 9, 2025

The Send your feedback form provides the user the option to categorize the feedback and provide additional details for the administrator to review. For example, consider if a user asks about their vacation policy and no answer is returned. The user can then choose the thumbs down icon and send feedback to the administrator.

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Generative AI Metadata Software Engineer Prompt Engineering 68

Snorkel AI

JUNE 23, 2023

This approach was less popular among our attendees from the wealthiest of corporations, who expressed similar levels of interest in fine-tuning with prompts and responses, fine-tuning with unstructured data, and prompt engineering.

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Data Science Large Language Models Data Scientist Machine Learning 64

Snorkel AI

JUNE 23, 2023

This approach was less popular among our attendees from the wealthiest of corporations, who expressed similar levels of interest in fine-tuning with prompts and responses, fine-tuning with unstructured data, and prompt engineering.

Data Science 64

Data Science Large Language Models Data Scientist Machine Learning 64

The MLOps Blog

SEPTEMBER 26, 2024

Effective mitigation strategies involve enhancing data quality, alignment, information retrieval methods, and prompt engineering. Broadly speaking, we can reduce hallucinations in LLMs by filtering responses, prompt engineering, achieving better alignment, and improving the training data. In 2022, when GPT-3.5

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LLM Prompt Engineering Prompt Engineer Auto-complete 72

Marktechpost

FEBRUARY 21, 2025

As a result, users often rely on prompt engineering to guide outputs, but this method lacks precision and does not provide insight into the model’s inherent response formulation. Existing techniques to refine LLM outputs include reinforcement learning, fine-tuning, and structured prompting.

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Large Language Models Prompt Engineering Prompt Engineer LLM 54