Marktechpost

JUNE 15, 2024

Current methods to counteract model collapse involve several approaches, including using Reinforcement Learning with Human Feedback (RLHF), data curation, and prompt engineering. RLHF leverages human feedback to ensure the data quality used for training, thereby maintaining or enhancing model performance.

AI Modeling 130

AI Modeling Prompt Engineering Prompt Engineer Large Language Models 130

AWS Machine Learning Blog

NOVEMBER 15, 2024

This includes handling unexpected inputs, adversarial manipulations, and varying data quality without significant degradation in performance. To learn more about CoT and other prompt engineering techniques for Amazon Bedrock LLMs, see General guidelines for Amazon Bedrock LLM users.

Responsible AI 107

Responsible AI Prompt Engineering Prompt Engineer AI 107

Flipboard

JANUARY 21, 2025

More generalist skill sets were helpful to cultivate further professional opportunities in the pre-AI era of work, but today businesses need specialists with deep expertise in specific work related to the tech, such as data extraction or data quality analysis. Relearning learning.

Data Mining 128

Data Mining Data Extraction Prompt Engineer Prompt Engineering 128

Unite.AI

JANUARY 11, 2024

In summary, text embeddings trained on LLM-generated synthetic data establish new state-of-the-art results, while using simpler and more efficient training compared to prior multi-stage approaches. With further research intoprompt engineering and synthetic data quality, this methodology could greatly advance multilingual text embeddings.

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 The process is inherently iterative, allowing for continuous improvement as new data or requirements emerge.

LLM 92

LLM Prompt Engineering Prompt Engineer Generative AI 92

Marktechpost

DECEMBER 8, 2024

Structured data is important in this process, as it provides a clear and organized framework for the AI to learn from, unlike messy or unstructured data, which can lead to ambiguities. Employ Data Templates With data quality, implementing data templates offers another layer of control and precision.

Prompt Engineering 112

Prompt Engineering Prompt Engineer Large Language Models LLM 112

ODSC - Open Data Science

MARCH 24, 2025

Gary identified three major roadblocks: Data Quality and Integration AI models require high-quality, structured, and connected data to function effectively. The Future of Analytics Careers in an AI-Powered World Given these shifts, what skills will be most valuable for future data professionals?

Automation 52

Automation Data Analysis Prompt Engineering Prompt Engineer 52

Snorkel AI

MARCH 4, 2025

LLM alignment techniques come in three major varieties: Prompt engineering that explicitly tells the model how to behave. Supervised fine-tuning with targeted and curated prompts and responses. Data quality dependency: Success depends heavily on having high-quality preference data.

LLM 52

LLM Large Language Models Data Quality Prompt Engineering 52

Marktechpost

DECEMBER 19, 2023

Researchers propose leveraging high-quality datasets like TinyGSM and a verifier model for optimal output selection from multiple candidate generations to achieve this. Filtering ensures data quality, excluding short problems or non-numeric content. By fine-tuning a 1.3B generation model and a 1.3B

Python 131

Python Natural Language Processing Prompt Engineering Prompt Engineer 131

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.

LLM 98

LLM Large Language Models ML Algorithm 98

Unite.AI

SEPTEMBER 4, 2023

Current Challenges with Llama 2 Data Generalization : Both Llama 2 and GPT-4 sometimes falter in uniformly high performance across divergent tasks. Data quality and diversity are just as pivotal as volume in these scenarios. Additionally, the license prohibits the use of LLaMa 2 for the improvement of other language models.

ChatGPT 290

ChatGPT Auto-complete Large Language Models LLM 290

ODSC - Open Data Science

NOVEMBER 20, 2024

This approach, he noted, applies equally to leveraging AI in areas like data management, marketing, and customer service. Right now, effective prompt engineering requires a careful balance of clarity, specificity, and contextual understanding to get the most useful responses from an AI model.

Generative AI 71

Generative AI Prompt Engineer Prompt Engineering Data Quality 71

Flipboard

JUNE 17, 2023

Surprisingly, most methods for narrowing the performance gap, such as prompt engineering and active example selection, only target the LLM’s learned representations. In particular, Tart achieves the necessary goals: • Task-neutral: Tart’s inference module must be trained once with fictitious data.

LLM 145

LLM Large Language Models NLP Prompt Engineering 145

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 108

Generative AI Machine Learning AI AI 108

Unite.AI

OCTOBER 16, 2023

Prompt Engineering : Engineering precise prompts is vital to elicit accurate and reliable responses from LLMs, mitigating risks like model hallucination and prompt hacking. Training Data : The essence of a language model lies in its training data.

Large Language Models 189

Large Language Models LLM Machine Learning Neural Network 189

ODSC - Open Data Science

MARCH 5, 2025

He identifies several key specializations within modern datascience: Data Science & Analysis: Traditional statistical modeling and machine learning applications. Data Engineering: The infrastructure and pipeline work that supports AI and datascience. Instead, they serve as powerful tools that can augment human capabilities.

Data Science 52

Data Science Software Engineer Data Scientist Machine Learning 52

The MLOps Blog

JUNE 27, 2023

W&B (Weights & Biases) W&B is a machine learning platform for your data science teams to track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results, spot regressions, and share findings with colleagues. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Unite.AI

JANUARY 19, 2024

Prompt Engineering This involves carefully crafting prompts to provide context and guide the LLM towards factual, grounded responses. Heavily depend on training data quality and external knowledge sources. Retrieval augmentation – Retrieving external evidence to ground content.

Large Language Models 113

Large Language Models LLM Prompt Engineering Prompt Engineer 113

Unite.AI

MARCH 13, 2024

Regardless of the approach, the training process for DSLMs involves exposing the model to large volumes of domain-specific textual data, such as academic papers, legal documents, financial reports, or medical records. While these efforts have made significant strides, the development and deployment of healthcare LLMs face several challenges.

Natural Language Processing 204

Natural Language Processing Large Language Models Data Scarcity LLM 204

TransOrg Analytics

OCTOBER 3, 2024

Data Observability for Real-Time Analysis In an era where real-time decision-making is critical, data observability will gain traction in 2024. Businesses will increasingly adopt data observability platforms that monitor the health of data pipelines, track data quality, and provide instant insights.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Data Integration Machine Learning 52

TransOrg Analytics

OCTOBER 3, 2024

Data Observability for Real-Time Analysis In an era where real-time decision-making is critical, data observability will gain traction in 2024. Businesses will increasingly adopt data observability platforms that monitor the health of data pipelines, track data quality, and provide instant insights.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Data Integration Machine Learning 52

AWS Machine Learning Blog

SEPTEMBER 9, 2024

Generative artificial intelligence (AI) has revolutionized this by allowing users to interact with data through natural language queries, providing instant insights and visualizations without needing technical expertise. This can democratize data access and speed up analysis.

Software Architect 113

Software Architect Generative AI AI AI 113

Snorkel AI

MARCH 4, 2025

LLM alignment techniques come in three major varieties: Prompt engineering that explicitly tells the model how to behave. Supervised fine-tuning with targeted and curated prompts and responses. Data quality dependency: Success depends heavily on having high-quality preference data.

LLM 59

LLM Large Language Models Data Quality Prompt Engineering 59

AWS Machine Learning Blog

APRIL 17, 2023

Prompt engineering Prompt engineering refers to efforts to extract accurate, consistent, and fair outputs from large models, such text-to-image synthesizers or large language models. For more information, refer to EMNLP: Prompt engineering is the new feature engineering.

Prompt Engineering 95

Prompt Engineering Prompt Engineer Deep Learning Machine Learning 95

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.

LLM 72

LLM Prompt Engineering Prompt Engineer Auto-complete 72

AWS Machine Learning Blog

SEPTEMBER 14, 2023

The complexity of developing a bespoke classification machine learning model varies depending on a variety of aspects such as data quality, algorithm, scalability, and domain knowledge, to mention a few. He is currently focused on Generative AI, LLMs, prompt engineering, and scaling Machine Learning across enterprises.

Categorization 116

Categorization Machine Learning Data Scientist Natural Language Processing 116

ODSC - Open Data Science

FEBRUARY 11, 2025

Inadequate Prompt Engineering: Prompts should be treated as critical components of the system, with version control and transparency to ensure consistent performance. Focus on data quality over quantity. Curated datasets can yield better results than massive, unfiltered datasets.

AI Engineer 52

AI Engineer Machine Learning Software Engineer Prompt Engineering 52

ODSC - Open Data Science

JULY 26, 2024

You’ll also be introduced to prompt engineering, a crucial skill for optimizing AI interactions. In particular, you’ll explore the criticality of data quality and availability, making data accessible through APIs, and techniques for making data GenAI-ready. Sign me up! Are you intrigued?

Data Scientist 52

Data Scientist Robotics Data Science Metadata 52

ODSC - Open Data Science

SEPTEMBER 20, 2023

ODSC West Confirmed Sessions Pre-Bootcamp Warmup and Self-Paced Sessions Data Literacy Primer* Data Wrangling with SQL* Programming with Python* Data Wrangling with Python* Introduction to AI* Introduction to NLP Introduction to R Programming Introduction to Generative AI Large Language Models (LLMs) Prompt Engineering Introduction to Fine-Tuning LLMs (..)

Data Science 52

Data Science Large Language Models Machine Learning Python 52

Bugra Akyildiz

AUGUST 3, 2024

Data Quality and Processing: Meta significantly enhanced their data pipeline for Llama 3.1: Data Quality and Processing: Meta significantly enhanced their data pipeline for Llama 3.1: Data Quality and Processing: Meta significantly enhanced their data pipeline for Llama 3.1:

Neural Network 52

Neural Network Prompt Engineering Prompt Engineer Large Language Models 52

O'Reilly Media

NOVEMBER 28, 2023

Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% AI users are definitely facing these problems: 7% report that data quality has hindered further adoption, and 4% cite the difficulty of training a model on their data.

Generative AI 137

Generative AI AI AI Data Analysis 137

Heartbeat

JANUARY 9, 2024

You can adapt foundation models to downstream tasks in the following ways: Prompt Engineering: Prompt engineering is a powerful technique that enables LLMs to be more controllable and interpretable in their outputs, making them more suitable for real-world applications with specific requirements and constraints.

Large Language Models 52

Large Language Models Natural Language Processing LLM Machine Learning 52

Snorkel AI

JUNE 9, 2023

Among other topics, he highlighted how visual prompts and parameter-efficient models enable rapid iteration for improved data quality and model performance.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Among other topics, he highlighted how visual prompts and parameter-efficient models enable rapid iteration for improved data quality and model performance.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Topbots

SEPTEMBER 11, 2023

For example, if you are working on a virtual assistant, your UX designers will have to understand prompt engineering to create a natural user flow. All of this might require new skills on your team such as prompt engineering and conversational design.

UX Design 59

UX Design AI AI Automation 59

Bugra Akyildiz

SEPTEMBER 8, 2024

Some of the other key dimensions and themes that they have improved upon with regards to model development: Data Quality and Diversity: The quality and diversity of training data is crucial for model performance. 👷 The LLM Engineer focuses on creating LLM-based applications and deploying them.

LLM 52

LLM Large Language Models Neural Network Machine Learning 52

The MLOps Blog

AUGUST 3, 2023

We have someone from Adobe using it to help manage some prompt engineering work that they’re doing, for example. We have someone precisely using it more for feature engineering, but using it within a Flask app. One of the features that Hamilton has is that it has a really lightweight data quality runtime check.

ML 52

ML Data Scientist Software Engineer Machine Learning 52