Tag: AI

Your GenAI, Your Data: How Local Models Put You Back in Control

When discussing GenAI one concern that consistently comes up is a worry around using public models and what happens to my data – there are genuine concerns about data privacy and security when using public AI models.  Luckily there are solutions that address these concerns that have been around for quite a while – Ollama , and Open WebUI – tools that empower organisations to run AI models on their own infrastructure.

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.

Mitigating Risks in LLMs: How Observability Enhances AI Reliability

Agentic AI and LLM tools enable remarkable capabilities, from automating workflows to generating content and insights. As I spend more time in Langflow and really start to appreciate the power of the systems that can be developed I started wondering about how they could be monitored, how do we implement Observability – then a note about Datadog LLM Observability came across my feed and got me thinking that this is worth looking at more deeply.

It’s all about the interface: How Natural Language Interfaces are Redefining the Way We Work

Mulling over why systems like ChatGPT, Perplexity, Gamma, Gemini, Claude etc. are so successful – it’s all about the interface. It isn’t just about their perceived intelligence – it’s about how they interact with us – it’s about the interface; the natural language interface . It’s a shift in how humans and machines collaborate. What does this mean as an approach to business.

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.

Beyond ChatGPT 4.0: Meet the Next Step in AI Assistance

(This week is an experiment to see what you think about the new ChatGPT o1-preview model output. I iterated through prompts to ask o1-preview to introduce itself and explain its abilities using my usual article format. The title of this article and all of the main article text below are 100% generated by the o1-preview model. What do you think? My opinion is at the end. For the record it took 29 seconds to generate the content.)

Identifying AI Risks: A New Tool for Businesses

Understanding the risks in any organisation or project takes time and usually involves one or more risk workshops, more often than not starting with a blank sheet of paper. Massachusetts Institute of Technology (MIT) have provided us with a short cut to identify risks associated with artificial intelligence using a new resource, the AI Risk Repository – save time and improve the breadth and depth of risks.