DataForce Blog

Academic LLM benchmarks often rely on prompting setups that don’t reflect real-world use. Learn why enterprises need more realistic evaluation methods.

Learn how to build custom LLM benchmarks that reflect real-world performance, avoid data contamination, and leverage human-in-the-loop evaluation.

We’re often testing LLMs on public datasets. Learn how custom datasets can offer a more accurate picture of real-world performance and avoid the limitations of standard benchmarks.

Train trustworthy AI models with human feedback, ethical datasets, and scalable RLHF. Align your LLMs and benchmark real-world performance.

As artificial intelligence continues to evolve, so too does the global conversation around its regulation. Governments, companies, and consumers alike are asking a pressing question—how can we ensure AI systems are safe, fair, and beneficial without stifling the pace of innovation? The answer is complex, but clear. AI regulation and AI innovation are not inherently at odds. In fact, they can reinforce each other.

As enterprises increasingly integrate generative AI into their operations, maintaining content quality is a growing priority. From chatbots and virtual assistants to code completion, organizations rely on this powerful technology to produce relevant, accurate, and useful outputs. However, evaluating the quality of AI-generated content at scale is no small feat—traditional assessment methods often fall short

As artificial intelligence continues to evolve, 2025 marks a pivotal year for innovation. Businesses are adopting AI at unprecedented rates to enhance operations, secure systems, and elevate customer experiences. Here are five AI trends set to redefine the future and drive technological advancements across industries.

To create inclusive, effective campaigns, businesses must integrate ethical considerations into their AI development and deployment processes. Here’s how they can achieve this.

As the holiday season approaches, businesses are gearing up for the annual surge in demand. With Black Friday marking the unofficial start of the shopping frenzy, AI technologies are playing a critical role in helping industries adapt to the challenges that come with increased customer activity.

Artificial intelligence is transforming the way businesses operate and how consumers interact with technology. But despite the significant growth, many consumers remain skeptical. There’s a growing concern that AI-driven products are not as trustworthy, safe, or fair as they should be. This skepticism stems from fears of bias, privacy breaches, and the perceived loss of human control in decision-making processes.

As businesses increasingly adopt generative AI for applications—ranging from customer service chatbots to content creation and complex problem-solving—it is important to ensure these systems deliver optimal performance to maintain user satisfaction and streamline business workflows. To achieve this, prompt engineering expertise is essential, as it is the key to leveraging generative AI successfully and harnessing the full potential of this technology.

When it comes to generative AI (GenAI), the role and value of data cannot be overlooked. Data powers these models, and the quality of training data significantly impacts the systems’ performance, accuracy, and overall quality. As the saying goes: “Junk in, junk out.” This is especially true in the field of GenAI, where models are trained to create new content.

The rise of electric cars marks a significant shift toward sustainability and innovation, with a projected 21% increase in electric vehicle sales in 2024. This shift is more than just switching the engine for an electric motor—it’s about redefining transportation.

The success of AI-powered products heavily relies on rigorous testing and continuous improvement. While advancements in machine learning have propelled innovation, human oversight remains crucial.

This innovative approach is reshaping how we train and instruct data contributors and annotators, enhancing generative AI training, and bringing ethical considerations to the forefront.

Companies operating within the realm of interactive entertainment and digital gaming are constantly looking for new ways to leverage AI to enhance the player experience. Conversational AI (CAI) offers developers this exact opportunity to boost player engagement and overall gaming experience.

From drug discovery to personalized medicine, AI systems are broadly transforming how we approach health and disease. The clinical development life cycle generates enormous volumes of data, and its potential is fully realized through the strategic application of AI tools, such as data annotation for medical imaging and adverse event detection. However, the integration of AI into such a sensitive and critical field comes with an ethical imperative: ensuring the AI systems we deploy are free from bias.

As AI models like ChatGPT, other types of LLMs, and generative AI applications have integrated into our frameworks, the spotlight on ethical AI and need for standards has intensified.

From advanced driver assistance systems to autonomous navigation capabilities, AI is reshaping the automotive industry. However, automotive manufacturers and suppliers must act to ensure consistent AI functionality across diverse markets and countries. This includes issues such as data localization, language adaptability, cultural sensitivity, real-world testing, collaboration with local partners, and ethical frameworks. By addressing these considerations, automotive companies can unlock AI’s full potential and create a unified future for vehicles worldwide.

In today’s fast-paced financial industry, data-driven decision-making has become increasingly important. Financial institutions are constantly looking for ways to improve their operations, better serve their customers, and stay ahead of the competition. To achieve these goals, they are turning to data to gain insights into customer behavior, market trends, and other factors that can impact their bottom line.