Organisations will turn to external partners for GenAI implementations and the success of your GenAI project depends heavily on the clarity of your requirements.
A well-prepared RFP with comprehensive requirements isn’t just bureaucratic paperwork – it’s your blueprint for AI implementation success. Let’s dive into some insights on creating requirements that will attract the right vendors and set your GenAI project up for success.
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.
In today’s business environment, staying ahead requires not only quick decision-making but also access to comprehensive, reliable information. GenAI Deep Research Models can help – they are AI driven tools designed to transform how businesses conduct in-depth research and analysis.
Before I get started on this one, it’s largely a thought exercise on where we could be taking Agents and the impact on the traditional API integration model.
The API landscape will shift. Just as APIs revolutionised how applications communicate, AI Agents are likely to emerge as the next solution for system integration. This transformation isn’t just about new technology – it’s about fundamentally changing how our software systems interact, learn, and evolve together.
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?
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…
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.
Am I living in a GenAI echo chamber? While my LinkedIn feed overflows with the latest AI breakthroughs and ‘must-try’ features, my experience in the trenches tells a different story. As a volunteer leading GenAI projects, delivering prompt engineering training and talking about GenAI in the non-profit sector, I’ve witnessed a gulf between the breathless pace of AI innovation and how most people actually use these tools day-to-day. (I have to say that I have not noticed resistance, concern – yes, but not resistance and in all cases I see the ‘wow’ moment happen when people realise the possibilities and practical applications.)
After completing certifications in the various platforms from IBM, Amazon Web Services (AWS), Microsoft and Google I wanted to take a high level look at what these platforms really mean for organisations. These organisations are offering powerful AI platforms that provide a way to access AI functionality without having to develop and run your own infrastructure and manage your own AI deployments.
(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.)
