Introduction to LLM Engineering: Building Agentic AI in Low-Resource Settings

Ronnie Kakunguwo — Thu, 30 Oct 2025 11:11:59 +0000

Hello everyone,

My name is Ronnie Kakunguwo, a Biomedical Engineer and AI researcher in training from Zimbabwe.

I am currently exploring how Generative AI (GenAI) and Large Language Models (LLMs) can be applied in healthcare for low-resource settings, particularly across Africa.

Over the past few weeks, I have been studying LLM Engineering, a growing field that sits at the intersection of machine learning, systems design, and prompt engineering. Through this short series, I will be sharing what I’m learning and experimenting with.

This first post introduces how these models work under the hood and how developers in low-resource environments can still build Agentic AI applications without large computational budgets.

What Are Large Language Models (LLMs)?

At their core, LLMs are large neural networks trained on vast amounts of text from the internet to predict the next word in a sentence.

You can think of them as compressed knowledge systems as they have read billions of pages and learned patterns in language, facts, reasoning, and even creativity.

A typical LLM, such as LLaMA 2 or GPT-4, is stored as two main components:

Parameters file: where all the learned knowledge (weights) is stored.
Run file: a small piece of code (in C or Python) that defines how those parameters are used during inference.

While training these models can cost millions, inference (using them) is relatively cheap and that’s where developers like us can contribute meaningfully.

The LLM Lifecycle: From Base Model to Assistant

LLMs typically go through three stages before becoming the assistants we interact with today:

Stage	Description	Data Used	Output
1. Pre-training	Model learns from billions of web pages to understand language patterns	Large web crawl (~10TB)	Base model (raw knowledge)
2. Fine-tuning	Model learns to respond helpfully and safely through curated Q&A datasets	~100k examples	Assistant model
3. RLHF (optional)	Reinforcement Learning from Human Feedback - humans compare model responses to improve quality	Human rankings	Refined assistant model

This is where the “assistant” aspect of AI is formed which is transforming a text generator into a system that aligns better with human intentions and provides more reliable, safe responses.

How Developers Can Work With LLMs in Low-Resource Settings

In countries like Zimbabwe and across much of Africa, we may not have the resources to train large models from scratch. However, we can still leverage open-source models and build locally relevant applications on top of them.

Here are a few approaches:

Use open-source base models like LLaMA 2, Mistral, or Gemma (Google) - powerful and freely available.
Apply lightweight fine-tuning techniques such as LoRA or QLoRA - these allow model customization even on limited hardware (for instance, Google Colab).
Integrate RAG (Retrieval-Augmented Generation) to connect your model to local data sources such as medical PDFs, hospital records, or reports.
Develop Agentic AI systems using frameworks like LangChain or LlamaIndex, enabling models to reason, plan, and use external tools.
Deploy efficiently on platforms like Google Gemini API, Ollama, or Hugging Face Spaces, which are affordable and easy to set up.

This bottom-up approach makes AI development more inclusive and practical - precisely what we need to strengthen African innovation ecosystems.

Why LLMs Matter for Low-Resource Healthcare

In regions where healthcare systems face resource constraints, Generative AI can augment healthcare professionals in meaningful ways.

For example:

A clinic assistant that summarizes patient data from SMS or WhatsApp messages.
A medication adherence system that sends reminders via USSD in local languages.
A radiology support tool that triages scans using open AI models before human review.

These are not distant ideas - they are achievable today with creative design, smaller models, and hybrid architectures that combine RAG and Agentic reasoning.

The Challenge: Safety, Security, and Bias

As powerful as they are, LLMs come with challenges:

Jailbreaks and prompt injection attacks that can bypass safety filters.
Data poisoning and embedded biases from low-quality datasets.
Privacy risks, especially in sensitive domains like healthcare.

Developers and researchers must therefore emphasize ethical design, data protection, and responsible deployment, especially in clinical or community-focused AI systems.

My Learning Journey and Invitation

I am currently learning through open materials, practical projects, and community collaborations, focusing on:

LLM Engineering
Agentic AI Systems
AI in Healthcare for Low-Resource Settings

I plan to continue sharing my findings, including both successes and mistakes as I explore this field further.

If you are a researcher, developer, or enthusiast working on similar topics, I would love to connect, exchange ideas, or collaborate.

Building the Future Together

LLMs are no longer just chat tools; they are becoming the core operating systems for the next generation of intelligent applications.

In low-resource settings, they present a unique opportunity to leapfrog traditional infrastructure and create tools that directly serve local needs.

If you have worked with RAG, fine-tuning, or Agentic AI in similar environments, please share your thoughts or feedback below.

I am still learning, and your insights will help refine my understanding and future explorations.

Author: Ronnie Kakunguwo

Biomedical Engineer | AI Research Explorer | Focused on Accessible Innovation in Africa

DEV Community: Ronnie Kakunguwo