You don't want a Vector Database

All of this interest in Vector Databases is driven by the explosion of Generative AI that ChatGPT sparked (less than 18 months ago!). Specifically, that Vector Databases enable the Retrieval Augmented Generation (RAG) paradigm, which is currently the leading form of prompt engineering and actually extracting business value from Large Language Models (LLMs).

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The missing piece in the AI application Stack - RAG Index Sharing

The evolution of AI from highly specific models to the broad-reaching capabilities of Large Language Models (LLMs) marks a pivotal shift. Imagine a landscape where data sharing unlocks AI applications that are specific to business use cases and secure.

Why hasn't AI taken over the Finance Industry?

AI has already proven to be a revolutionary force around the world. From deciphering dead languages to finding previously unknown patterns in historical data, it is redefining our understanding of our past. With content creation and recommendation systems on platforms we use daily it changes the way we view our present. And with assisting on health diagnosis and the creation of new pharmaceutical drugs it is already changing our shared future. Silicon Valley is also betting big on AI as the most important tech breakthrough since the smartphone. For all of this, AI has yet to be a revolutionary force in the financial services industry (FSI). Despite the huge amount of blogs, articles, and papers dedicated to the subject, as someone who’s job it is to watch for fintech trends around the world I have been extremely underwhelmed by the so called “impact” of AI in financial services. Unlike the humanities, entertainment, and healthcare industries listed above, when you read any article on the impact of AI in finance it usually comes down to a few key areas: credit decisioning, fraud and AML, and (sigh) chatbots. The lack of imagination is not the author’s faults - there are simply very few real world examples thus far of AI working in the financial industry. The better question then “what is possible” for AI in finance is “why isn’t finance adopting AI more quickly?”.

4 things product leaders shipping AI capabilities need to be aware of

There are a number of new requirements & technologies that PMs need to factor into their roadmap in order to build an AI app, outside the norm of ‘traditional' SaaS application development.

Looking to use LLMs to unlock productivity in your company? Managing data permissions will accelerate value

The core value of a business is in its intellectual property that is secured behind data permissions in a variety of tools (Confluence, Sharepoint, Gdrive, salesforce all have different permission models). While generalisable LLMs are extremely powerful, context about how a business operates, their processes and their customers are vital to solve problems.

The challenges with building an AI product with transparency at its core

One of the fundamental challenges is a lack of user trust in AI. This skepticism isn't baseless but rooted in the perceived opacity of AI operations. To overcome this, adopting transparency in the development and operation of AI products is fundamental.

Computer, Enhance: How to Think About LLMs Effectively

The size of the problem-space addressed by LLMs may seem dizzying at first glance; but with some deeper reflection, there's a clear way to think about them that's both simple and powerful. Having a mental framework in place to organise your approach to building next-gen AI apps will provide your designs with improved clarity, give you a coherent methodology for iterating on them, and reduce the time to shipping your product. This mental framework all comes down to the idea of fidelity.