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  • Writer's pictureNathan Temeyer

RAG to Riches: How Data Engineering Empowers Micro-Business Growth

Updated: Jan 30

Tackling Real-World Challenges in Business Coaching

In the dynamic world of micro and small enterprises, a timely and personalized approach to business coaching is crucial. Markets are dynamic, volatile, and opaque - especially for female migrant entrepreneurs that are navigating new terrain, culture, and possibly languages. Today, we talk about how Retrieval-Augmented-Generation (RAG) is emerging as a vital tool in this arena. Building upon our previous article about ONOW's innovative approach to financial tracking, we're diving into a technical component that has helped us overcome the generic responses of base tools like ChatGPT, reduces hallucinations, and supercharges our business support for coaches and business owners alike.

Streamlining Decision-Making with Data

Many readers will likely be familiar and experienced with ChatGPT. And while incredibly powerful and innovative as a general tool, we found it can't meet the unique challenges of our target audiences out-of-the-box. It needs help.

Retrieval-Augmented-Generation (RAG) is a quickly developing field that fixes this issue by retrieving useful information, and augmenting the standard generation process towards more unique paths that address the specific needs of entrepreneurs in challenging environments. Rather than using the data from the entire internet, we're using specific pieces of highly-valuable information to prime the model, and then using the LLM to package it in a user-friendly way. Imagine dynamically selecting business owner data, business coach input, news from current events, and context about a country or specific location that gives hyper-personalized context to an AI engine. This engine can now use its power on a specific corner instead of traversing the entire knowledge base of human history.

As we gain more information from coaching calls, educational journeys, and financial tracking, it gives the business owner a very specific, and personalized path without the need to prompt ChatGPT to reach the same corner. We've automated that journey to get the value immediately. This translates into better financial management, improved marketing strategies, personalized learning, and enhanced operational efficiencies.


While a full demonstration is beyond the scope of this article, the examples below demonstrate the difference between a ChatGPT and RAG-augmented GPT process for a coach looking to help a handicraft business operating in Yangon:

Basic GPT Advice:

While useful, we find this advice to be fairly generic and of limited value. Simply asking ChatGPT to provide advice to a handicraft business in Yangon will indeed direct it towards useful advice in this situation, but it's not specific enough to generate something truly impactful. Through experience, we've found that providing additional information over the course of a conversation can gradually improve upon this deficiency, but it runs into the challenge of poor user experience. Our target audience doesn't have the time, energy, or expertise to patiently provide GPT with every detail of their business. This is a process that can be automated with RAG!

RAG-Augmented Process:

This process utilizes more detailed information about the business owner, the strengths/weaknesses of the business, information about the entrepreneurial environment of Myanmar, and current events and news before generating an action step. In this case, the advice is intentionally formatted to be within the same topic, and suggests establishing an online presence, but you can clearly see an increased nuance and detail that comes from the RAG process. In practice, this is combined with proper channels and UX-features that further improve communication with our target audience, as well as a connection with a real-life coach to guide them through a complex situation with a human touch.

The technical challenge is arranging the proper data engineering processes to determine the highest "signal-to-noise" ratio that gives us maximally-impactful results. We can choose to use AI to help us automate this process, combine with predetermined criteria, or combine these methods depending on the use case.

Imagine a business coach using this process compared to the previous example as they prepare for a coaching call with this hypothetical business owner. Their overhead time for preparation is dramatically reduced, and the output is far more detailed. This is the same process that can be used to personalize learning journeys and financial tracking.

Looking Ahead: Practical AI Solutions for Business Growth

As we continue to explore and develop AI-driven solutions with RAG, the focus remains on delivering real, tangible benefits to the MSMEs we serve. It's about providing practical tools that address the everyday challenges of running a small business and helping entrepreneurs to unlock their full potential. We live in a time where AI and digital tools are progressing at dizzying speeds, and we have a privilege to address some truly difficult problems with MSMEs around the world with tools like these.

Stay tuned for more updates as we further develop these innovative solutions, as we seek to provide a coach for every business!

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