Insights
Practical Security Guardrails for Large Language Models
Actionable techniques to ensure secure LLM deployments that balance innovation with function, from using prompt injection protection to ethical use and access controls.
The Dimensions of Enterprise AI Governance: A Focus on Model Lifecycle Management
Explore how structured model lifecycle management turns governance principles into an operational reality, helping to guide AI development from design through retirement with control, transparency and trust.
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.
Unlocking AI's Potential: The C-Suite Blueprint for Responsible Innovation
A C‑level framework to adopting AI responsibly, balancing innovation with risk, oversight and scalability to achieve fast and ethically scale solutions.
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.
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.
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.