AWS Machine Learning Blog

MARCH 20, 2025

Next, Amazon Comprehend or custom classifiers categorize them into types such as W2s, bank statements, and closing disclosures, while Amazon Textract extracts key details. With growing content libraries, media companies need efficient ways to categorize, search, and repurpose assets for production, distribution, and monetization.

Automation 105

Automation IDP Generative AI Prompt Engineering 105

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.

Generative AI 105

Generative AI Prompt Engineering Prompt Engineer AI 105

AWS Machine Learning Blog

DECEMBER 23, 2024

Lettrias in-house team manually assessed the answers with a detailed evaluation grid, categorizing results as correct, partially correct (acceptable or not), or incorrect. Results are then used to augment the prompt and generate a more accurate response compared to standard vector-based RAG.

Generative AI 123

Generative AI Natural Language Processing Prompt Engineering Prompt Engineer 123

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.

Prompt Engineering 106

Prompt Engineering Prompt Engineer Natural Language Processing Machine Learning 106

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.

Automation 120

Automation Prompt Engineering Prompt Engineer Categorization 120

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.

Large Language Models 127

Large Language Models Categorization LLM Explainability 127

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.

Business Intelligence 115

Business Intelligence Prompt Engineer Prompt Engineering Natural Language Processing 115

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.

Large Language Models 147

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.

LLM 92

LLM Prompt Engineering Prompt Engineer Generative AI 92

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.

Prompt Engineer 116

Prompt Engineer Prompt Engineering Large Language Models LLM 116

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.

Software Engineer 112

Software Engineer LLM Large Language Models Categorization 112

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.

Large Language Models 108

Large Language Models Prompt Engineering Prompt Engineer Categorization 108

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?

Large Language Models 98

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.

Generative AI 118

Generative AI Prompt Engineering Prompt Engineer Computer Vision 118

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.

Categorization 116

Categorization Machine Learning Data Scientist Natural Language Processing 116

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.

Generative AI 107

Generative AI Machine Learning AI AI 107

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.

LLM 89

LLM Generative AI Automation Machine Learning 89

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.

Auto-complete 240

Auto-complete ChatGPT Large Language Models Neural Network 240

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.

LLM 83

LLM Prompt Engineer Prompt Engineering Large Language Models 83

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.

Large Language Models 113

Large Language Models LLM Prompt Engineering Prompt Engineer 113

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.

Natural Language Processing 127

Natural Language Processing LLM Prompt Engineering Prompt Engineer 127

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.

Generative AI 123

Generative AI Prompt Engineering Prompt Engineer ML 123

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.

IDP 132

IDP LLM Prompt Engineering Prompt Engineer 132

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.

Generative AI 105

Generative AI Auto-complete Software Development Automation 105

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.

Prompt Engineering 245

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

Prompt Engineering 64

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.

Large Language Models 103

Large Language Models Natural Language Processing Artificial Intelligence Artificial Intelligence 103

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.

Chatbots 122

Chatbots Automation Machine Learning DevOps 122

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.

OpenAI 52

OpenAI Large Language Models Categorization LLM 52

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

LLM 72

LLM Prompt Engineering Prompt Engineer Auto-complete 72

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

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

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.

Large Language Models 53

Large Language Models Prompt Engineering Prompt Engineer LLM 53

Marktechpost

FEBRUARY 13, 2025

Conventional strategies for reducing AI biases, such as fine-tuning, prompt engineering, and reinforcement learning using human feedback, are based on top-down intervention by developers. Seven prevailing strategies were identified as user-driven alignment strategies, which were categorized into three broad approaches.

AI 61

AI AI Prompt Engineering Prompt Engineer 61

ODSC - Open Data Science

OCTOBER 27, 2023

The core idea behind this phase is automating the categorization or classification using AI. We use Amazon Textract’s document extraction abilities with LangChain to get the text from the document and then use prompt engineering to identify the possible document category.

IDP 98

IDP LLM Large Language Models Data Science 98

Marktechpost

DECEMBER 2, 2024

Current methods for query rewriting in RAG systems can be broadly categorized into two- training-based and prompt-based approaches. Training-based methods involve supervised fine-tuning using annotated data or reinforcement learning, while prompt-based methods use prompt engineering to guide LLMs in specific rewriting strategies.

Large Language Models 44

Large Language Models Prompt Engineering Prompt Engineer Categorization 44

Snorkel AI

AUGUST 17, 2023

Users can easily constrain an LLM’s output with clever prompt engineering. That minimizes the chance that the prompt will overrun the context window, and also reduces the cost of high-volume runs. Its categorical power is brittle. The former will make the generative model’s outputs (mostly) fall into an expected range.

Generative AI 52

Generative AI BERT LLM Prompt Engineering 52