Microsoft’s recent Build 2025 announcements have brought some massive updates to the whole GenAI Copilot platform. One of the most exciting features for me (and perhaps a revealing feature in terms of Microsoft’s GenAI strategy) is the introduction of Microsoft 365 Copilot Tuning. This new feature is set to revolutionise how organisations tailor AI to their specific needs, moving powerful model fine-tuning capabilities from the often complex of data scientists and technical staff to the more accessible world of business power users.
Category: Solution Design
Is Your Data ‘Good Enough’ for Generative AI decision making? Spoiler: It Might Be!
Generative AI (GenAI) is promising to reshape how we work and innovate. Yet, many organisations hesitate, wondering, “Is our data perfect enough?” However, perfection often isn’t the prerequisite you think it is.
Why Design is About to Change: GenAI and the Future of App Development
A number of threads are converging:
Coding assistants like GitHub Copilot, Codeium, Cursor, and Replit Ghostwriter are integrated directly into IDEs – or in some cases, are the IDEs.
Autonomous agents or agent environments that can generate and execute code, like Microsoft Magentic-One or Langflow .
Application generation environments like Replit takes this even further, generating full applications from natural language prompts and deploying them with just a few clicks.
Agentic AI Design: Balancing Autonomy and Control
Agentic AI seems to be talked about constantly and as I’ve talked about in previous articles design patterns matter – same thing applies to AI Agents, design matters. What design patterns, or a least design considerations are there for Agent AI design?
The Blueprint for GenAI Success: Why GenAI Architecture Frameworks and Design Patterns Matter
While mulling over what seemed to be a lack of GenAI frameworks and design patterns last week, this post from Debmalya Biswas dropped into my feed on A Comprehensive Guide to Agentic AI. This post and the accompanying slides are more than the title suggests – they are a great start on a set design patterns and reference architectures – something that we seem to be sorely missing.
Agentic AI – what would heading into 2025 be like without another article on Agentic AI.
We’ve been seeing Agentic AI hit the press a lot over the last while, especially as we head into 2025. I recently had the chance to build a PoC and find out what Agentic AI was all about and I think I get it now…
LLM Security – The OWASP Top 10 for LLMs & What You Need to Know
As AI continues to revolutionise industries, understanding and mitigating the security challenges around large language models (LLM’s) is critical. The OWASP Top 10 for LLM’s is a comprehensive guide to the most pressing risks faced by these models.
There is value in that unstructured data.
For quite some time I have been concerned about the unrealised value in unstructured data – the myriad of Word documents and PDF’s that contain everything from organisational policies to processes and reports (we aren’t talking about video, images and audio in this article). This increasing amount of unstructured data and the ability to absorb it is one of the things that increases the time that new hires take to become effective or means that a policy (if not encapsulated within a system) does not get adhered to.
ISACA Announces AI Audit Toolkit: What You Need to Know and Why it’s Good for System Design…
In a significant move to bolster AI governance and compliance, ISACA has unveiled its AI Audit Toolkit. This toolkit is designed to help organisations navigate the complexities of auditing AI systems, providing a structured approach to assess and ensure their AI technologies are both effective and ethically sound. Although focused on Audit, why not use it to help drive design work?
AI’s Role in Reducing Risk in the SDLC (e.g. CrowdStrike)
In the wake of the recent CrowdStrike incident it’s easy to become an armchair critic. For those with experience in IT, isn’t it likely that such issues are multi-dimensional, spanning technical, managerial, cultural, and even simple human errors?