About a-team Marketing Services

A-Team Insight Blogs

The Potential and Pitfalls of Large Language Models

Subscribe to our newsletter

By Tony Seale, Knowledge Graph Engineer at Tier 1 Bank.

Large Language Models (LLMs) like ChatGPT possess enormous power, stemming from their capability to ingest and compress vast amounts of general information gathered from the web. However, this capability is general rather than tailored to your specific business needs. To effectively utilise these models in a context relevant to your business, it’s essential to provide them with specific information and data related to your sector and niche. After all, if the general LLM knows everything your business knows – what’s the point of your business? But here’s the kicker: if you put garbage in, you get garbage out. Disorganised data will result in vague or even inaccurate answers.

We can state that the quality of your AI offering will directly depend on the quality of the data you input into the LLM. In other words, the quality, connectivity, organisation, and availability of information within your organisation are key factors in determining the success of your main generative AI use cases. However, there is a harsh truth to acknowledge; the data estates of most large organisations are currently very disorganised.

Given that the organisation of our data is directly related to the quality of our LLM’s responses, perhaps our primary AI strategy should actually be to double down on our data strategy!

Organising your total data estate is no trivial task, but I believe the great AI acceleration will soon make it necessary. While there are no simple answers, here are some links offering insights into building a semantic data mesh, an architectural blueprint that could help you navigate this complex journey:

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Streamlining trading and investment processes with data standards and identifiers

Financial institutions are integrating not only greater volumes of data for use across their organisation but also more varieties of data. As well, that data is being applied to more use cases than ever before, especially regulatory compliance and ESG integration. Due to this increased complexity of institutions’ data needs, however, information often arrives into...

BLOG

EU’s AI Act Seen Strengthening Data Foundations but not Without Challenges

The European Union’s Artificial Intelligence Act, which went into force this month, has presented financial institutions with huge opportunities but also some grave challenges, each of which can only be managed with a strong data foundation. Industry professionals have said that the Act’s provisions, though extensive, can bring clarity to a muddled regulatory view of...

EVENT

AI in Capital Markets Summit New York

The AI in Capital Markets Summit will explore current and emerging trends in AI, the potential of Generative AI and LLMs and how AI can be applied for efficiencies and business value across a number of use cases, in the front and back office of financial institutions. The agenda will explore the risks and challenges of adopting AI and the foundational technologies and data management capabilities that underpin successful deployment.

GUIDE

AI in Capital Markets: Practical Insight for a Transforming Industry – Free Handbook

AI is no longer on the horizon – it’s embedded in the infrastructure of modern capital markets. But separating real impact from inflated promises requires a grounded, practical understanding. The AI in Capital Markets Handbook 2025 provides exactly that. Designed for data-driven professionals across the trade life-cycle, compliance, infrastructure, and strategy, this handbook goes beyond...