The art of crafting an AI strategy through Wardley Maps — Background — Spearheading AI Strategy at an Established Bank Prior to founding Data-Centric Solutions, I spearheaded the development, execution, and implementation of the AI strategy at an established bank. Although relatively green in delivering at such scale, I accepted the job with enthusiasm. Dispelling the initial feelings of imposter syndrome, I reassured myself that the worst-case scenario would involve…

Exploring Tactics to Resolve the Challenge of Enterprise Jargon for RAG systems — The Challenge of Enterprise Jargon Retrieval augmented generation (RAG) has become a popular method for overcoming the inherent limitations of large language models (LLMs). The effectiveness of this approach has opened a myriad of possibilities for engineers to develop LLM-powered applications for enterprises. Inevitably, when developing such tools, engineers encounter issues with enterprise jargon. By…

How Data Analysts Will Evolve Over the Next Five Years — The Fate of the Superstar Data Analyst If you’ve had the fortune of working with a good data analyst, you’ll know that they’re invaluable. This person is a fountain of knowledge for all your enterprise data needs, from getting access to data to navigating the mishmash of enterprise data sources. They leverage their well-honed SQL and dashboarding…

How RAG is Helping to Overcome the Limitations of LLMs — RAG is in Vogue, for Good Reason If you have been dabbling in the world of large language models, you may have come across the term RAG — short for retrieval augmented generation. RAG is in vogue right now, at an AWS conference I attended recently, about 80% of the engineers I spoke to were working on…

An exploration of Realistic and High Impact LLM Applications Feasible Today — What I relish most about running an AI consultancy is the unique perspective it grants me. At the time of writing, I can confidently say that numerous business leaders recognise the transformative power of large language models and their potential to revolutionise the businesses they oversee. …

Why The Emergence of AGI in LLMs is More Hype than Reality (For Now) — Artificial General Intelligence Many have been wowed by the capabilities of large language models (LLMs), especially following the success of ChatGPT. We’ve collectively witnessed a display of computer intelligence that is eerily reminiscent of science fiction. This, coupled with the fact that large language models are essentially black boxes, has sparked the imaginations…

Using Retrieval and Function Calling to Extract and Structure Data from Long Documents — Manual Document Processing With all the AI innovation happening, it’s easy to forget that many aspects of people’s jobs are still manual and tedious. Much of this tedium lies in repetitive document processing. While working for a bank, a former stakeholder of mine, who was a risk manager, was inundated with manual document…

How Retrieval Augmented Generation Can Enhance Your LLMs by Integrating a Knowledge Base — The Limitations of ChatGPT ChatGPT is limited for many practical business use cases outside of code generation. The limitation arises from the training data, and the model’s propensity to hallucinate. At the time of writing, if you try to ask the Chat-GPT questions about events occurring after September 2021, you will probably receive a…

How you can fine-tune your LLMs with limited hardware and a tight budget — Demand for Bespoke LLMs With the success of ChatGPT, we have witnessed a surge in demand for bespoke large language models. However, there has been a barrier to adoption. As these models are so large, it has been challenging for businesses, researchers, or hobbyists with a modest budget to customise them for their own…