Enhance your customer experience, empower users to create and share custom copilots, and proactively identify and mitigate risks with Fuel iX by TELUS Digital. Discover how more than 50,000 users are leveraging our award-winning generative AI engine to achieve results.
Discover five critical strategies for mitigating bias when implementing generative AI into your organization.
Navigating the challenges of artificial intelligence through an orchestrated approach
A recent TELUS Digital survey explored consumer sentiment around the origin and quality of generative AI training data. Take a look at the findings.
The Supervisor LLM moderation technique uses one large language model to filter and moderate the content generated by another to prevent harmful outputs.
These 3 real-world examples show how generative AI in healthcare boosts physician productivity, lowers costs, and improves patient outcomes.
AI powers more efficient and intuitive digital healthcare experiences.
A CIA technique called a canary trap helps us detect AI hallucination risk in large language models (LLMs) enhanced with retrieval augmented generation (RAG).
As use of generative AI increases, it's critical to be able to understand how models arrive at the output they do in order to foster trust.
Integrating continuous evaluation of large language models (LLMs) into your CI/CD pipelines keeps undesired changes from impacting your generative AI solutions.
Learn what retrieval augmented generation (RAG) is, how it enhances generative AI like large language models (LLMs), and key considerations for RAG systems.
Intent classification used in concert with a large language model (LLM) and retrieval-augmented generation (RAG) system resulted in a safer financial chatbot.
Large language models (LLMs) are effective tools for testing how well retrieval augmented generation (RAG) systems can enhance a generative AI model.
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