Insights
The Evolution of Enterprise Apps in the Generative AI Era
Learn about how enterprise applications are evolving with GenAI to become more intelligent, adaptive and embedded into daily decision-making in business.
Why Your Enterprise Needs a Unified Approach To Generative AI
Discover why a strategic, enterprise-wide AI strategy is essential to deliver real value, with tangible support, security and usability across the business.
Red Teaming Large Language Models: A Critical Security Imperative
“Red teaming”, a military approach to providing structured challenges to plans, policies and assumptions, has some key uses in technology: from exposing vulnerabilities in LLMs to ensuring safe, secure, and ethical deployment at scale. Learn how we use “red teaming” here at WeBuild-AI.
5 Essential Best Practices for LLM Governance: A Framework for Success
Key practices for organising, monitoring and securing large language model systems in enterprise settings.
How We Built An AI Launchpad in Under 20 Days on Amazon Web Services
Co-founder of WeBuild-AI, Mark Simpson, shares how WeBuild‑AI built our “Pathway” launchpad using AWS and generative AI, completing over 200 deployments in 20 days to validate our product architecture.
Measuring Speed and Efficiency in LLMs
Explore key metrics and benchmarks to evaluate large language model performance, from latency to cost and enterprise-wide impact.
Embracing Model Diversity: Why Organisations Should Adopt Multiple Large Language Models
Learn why using multiple LLMs can enhance resilience, performance and innovation across enterprise AI applications.
Key Safety Features for Creating AI-Enabled Products with Amazon Bedrock
Explore Amazon Bedrock's essential safety features for responsible AI deployment. Learn how guardrails like content filters, denied topics, and contextual grounding checks mitigate risks in AI-enabled products. Discover how these features prevent incidents like chatbot jailbreaking and misinformation, ensuring compliance and protecting brand reputation. Ideal for technology decision-makers seeking to innovate with AI while prioritising safety and ethics in an era of increasing AI capabilities and public scrutiny.
Generative AI - With Great Power, Comes Even Greater Responsibility
Explore the essential steps for governing generative AI in this blog by Ben Saunders. As generative AI becomes a powerful tool for innovation, it's crucial to establish robust guardrails and controls to prevent unintended consequences. Learn about the potential risks of unrestricted AI use, including ethical and legal implications, and discover how to implement technical controls and governance frameworks to ensure responsible AI deployment. Stay ahead in the digital age by adopting effective governance strategies that balance innovation with accountability.
To Fine Tune, or Not to Fine Tune, That is the Question - How LLMOps Can Help
In the rapidly advancing field of Artificial Intelligence, particularly with Large Language Models (LLMs) from OpenAI, Google, and others, fine-tuning these models remains essential. This blog explores why fine-tuning is crucial for industry-specific applications, enhancing customer experience, and boosting employee productivity. It delves into LLMOps, a specialized framework ensuring efficient, reliable, and compliant operations of LLMs. By focusing on data management, model development, prompt engineering, deployment, observability, ethical evaluations, and reinforcement learning, organizations can harness LLMs' full potential while maintaining regulatory compliance and operational excellence.
LLMOps on AWS: Mastering Large Language Model Operations with Amazon Bedrock
Explore how to operationalise LLMs using AWS tools, with best practices for scalability, observability and secure deployment.