Emerging technologies such as Artificial Intelligence (AI), robotics and others are gaining global traction and redefining the ways people live their lives. These technologies are redefining every facet of human endeavours including health, agric and others. Ayodeji Adeniyi, a full Stack LLM Development Specialist, provides insight into all the pros and cons of AI. Lucas Ajanaku reports.
Explaining what generative AI is and how it is different from other types of AI, he said it is a branch of AI that creates new content, such as text, images, audio, video, code, or data. While traditional AI focuses on analyzing or classifying existing information (such as detecting fraud or filtering spam), generative AI produces new outputs. For example, it can write an article, generate an image, or synthesize speech.
On the key skills and technologies required to become a generative AI engineer, he said strong programming skills, especially in Python, are essential. Python is widely used in AI development due to its rich ecosystem of libraries and frameworks. You also need a solid grasp of machine learning, deep learning, and frameworks like TensorFlow or PyTorch, particularly if you want to train or fine-tune models. Familiarity with tools like Hugging Face Transformers, LangChain, and vector databases is also highly valuable.
Could generative AI models be integrated into software applications, Adeniyi said it depends on the use case. “Typically, I use pre-trained large language models (LLMs) and build a backend, often in Python, that connects the model to a frontend interface. For domain-specific needs (like finance or healthcare), I fine-tune models with relevant data to improve their contextual accuracy. This results in smarter and more reliable applications,” Adeniyi who is a Generative AI Engineer and software developer with a background in Mechanical Engineering and driven by a deep passion for AI said.
It is not uncommon to face challenges when developing generative AI systems. He said accuracy is a major challenge; generative models can sometimes produce incorrect or misleading responses. Ensuring reliability across different scenarios takes thorough testing, prompt engineering, and continuous tuning. Other challenges include data privacy, computational cost, and scalability.
In every innovation, ethics is vital. Ensuring a healthy handshake between generative AI and projects, he said is crucial. Adeniyi ensures this by implementing “guardrails”, safety mechanisms that prevent misuse. This includes content filtering, bias mitigation, and avoiding the generation of harmful or sensitive information. “I also ensure that systems follow ethical guidelines and comply with data protection regulations,” he added.
On recent project where he used generative AI and the impact it had, Adeniyi gave a few highlights: “One of them is QuidxAI, an AI mentor for forex and crypto traders, which I developed in collaboration with a talented team of Nigerian engineers.
“Another project was a customer support bot for the telecom industry. Unlike traditional chatbots, which are programmed to follow a rigid flow and where users only select options by pressing a number, this one can hold natural conversations, understand the user’s need, match their intent correctly, and even speak to users with a human-like tone. It helps reduce wait times by handling basic issues, while serious cases are handed over to a live agent.
“Another project was in the entertainment space. My team built a smart itinerary planner for a world-famous amusement park that helps visitors plan affordable, time-efficient experiences.
“I also worked on a personal project, a healthcare solution; a senior care bot that scans consumables for chemical risks and offers conversational support to elderly users. It provides both safety and companionship.”
Speaking about his odyssey into the tech world, he said it began with a natural curiosity about intelligent systems and how they can be used to transform lives and industries. This, he said led him to transition from mechanical engineering into software development, eventually earning a Master’s degree in Software Systems Design.
“Throughout my career, I’ve worked on a variety of AI-powered applications and full-stack systems, with a growing focus on Generative AI. I’m particularly interested in building intelligent tools that improve decision-making, streamline workflows, and enhance user experiences.
Recently, I led a team of experts—including a medical doctor, two biomedical engineers, and a computer scientist—to develop an innovative project with a working titled called CardioSense.
He also served as the Generative AI and full-stack developer on the team. The project was part of a global hackathon at Massachusetts Institute of Technology (MIT) Boston and the competition welcomes participant from all over the world. The innovation falls under the Heart Health track. It’s an AI-powered telemonitoring platform for cardiovascular patients. It integrates with smartwatches and other Internet of Things (IoT) health-monitoring devices to deliver real-time, AI-driven insights to clinicians and caregivers. The system also uses intelligent agents to facilitate personalized follow-ups and early detection of cardiac anomalies through proactive alerts.
CardioSense finished in the top three of its track and was recognized by Google as one of the top ten innovations of the entire competition. For me, that recognition affirmed not just the value of the technology, but also the impact of cross-disciplinary collaboration in solving critical health challenges.
With laser focus on AI, he said he is currently focused on building more AI-driven systems that bridge the gap between people and technology, especially in areas such as healthcare, education, and personal productivity. “My work sits at the intersection of engineering, software, and human-centered design—and I’m excited about what the future holds,” he said.
Globally, data privacy and security have become great issues. Data privacy, also known as information privacy, refers to the responsible handling of sensitive data, particularly personal information. It encompasses the proper storage, access, retention, and security of this data to prevent unauthorized access, use, or disclosure.
In Nigeria, the Nigeria Data Protection Act (NDPA) 2023 is the primary legislation governing data privacy, aiming to protect fundamental rights and freedoms related to personal data. Nigeria Data Protection Commission (NDPC) was established under the Nigeria Data Protection Act 2023.
The Nigeria Act came after General Data Protection Regulation (GDPR), a European Union (EU) regulation focused on data protection and privacy for individuals within the EU. It was designed to give individuals more control over their personal data and modernizes data protection rules for businesses.
How does this expert integrate data privacy and security in the field of generative AI?
Adeniyi said: “We implement strict privacy controls. Personal data is masked or excluded from model inputs, and we never use user data for training without explicit consent. All sensitive data is encrypted, and we follow data protection laws like GDPR and NDPR. Transparency is key; users should always know how their data is being used.”
Some programming languages and frameworks are used for building generative AI models. Adeniyi said he primarily uses Python due to its readability and ecosystem. “For AI development, I use tools like LangChain, FastAPI, CrewAI, Google Dialogflow, OpenAI SDK, LangGraph, and more. These allow for rapid prototyping and deployment of LLM-based apps and autonomous agents,” he explained.
To keep up with the rapidly evolving field of AI and machine learning, he said he ssubscribes to newsletters, read AI blogs on Medium, watch YouTube tutorials, and follow researchers online. “I also attend tech meetups, join AI hackathons, and take online courses. These help me keep up with new tools, trends, and techniques,” he said.
His advice to software engineers interested in specializing in generative AI is for them to start small and stay consistent. “Generative AI might seem intimidating at first, but it’s accessible if you take it step by step. Leverage free resources, YouTube, official documentation, and community forums, and build mini projects to practice. Curiosity and persistence go a long way,” he said.
There have been so much conversations around AI and job losses. People are afraid, and rightly too, that one day, they will wake up and discover that they no longer have a job as these technologies move fast to replace the human element.
Adeniyi rather believes AI can be a veritable tool in the transformation of manufacturing, health, agric and even the security architecture in Nigeria for precision and efficiency. According to him, in manufacturing, GenAI can automate paperwork, generate machine part designs, and improve efficiency. Himself as someone who transitioned from Mechanical Engineering, he said he recently saw an awesome AI tool that reminded him of AutoCAD, but instead of manually having to design mechanical parts by yourself, this AI tool lets you design machine parts just by typing descriptions, and at the same time allows manual editing, like a smarter AutoCAD.
In healthcare, it can help doctors reduce time spent on paperwork and focus more on patients. “For example, I led a team as a GenAI engineer at an MIT AI hackathon where my team built a wearable-integrated AI system for monitoring heart patients remotely, which won third place.
Gen AI is revolutionizing agriculture by offering tools for precision farming, improved crop management, and enhanced sustainability. “GenAI can provide insights on weather, crop health, and irrigation,” he explained.
GenAI analyzes data from various sources to provide tailored recommendations for planting, and fertilization, optimizing resource use and boosting yields to enhance food security. Additionally, GenAI aids in disease detection, yield prediction, and even the development of new crop varieties.
For security, AI can assist with surveillance, threat detection, and misinformation tracking. Generative AI is rapidly transforming homeland security by enhancing various operations, including threat detection, operational efficiency, and decision-making.
Just like every other deployment of technology, there are potential risks and benefits of deploying generative AI in real-world applications. He said the benefits include accelerated productivity; automation of routine tasks; enhanced decision-making; and scalable solutions.
Risks also are just like every good tool in the wrong hand, it can be used in the spread of misinformation or deepfakes.
It can also be used to get biased or misleading outputs; while data privacy remains a major concern.
These risks, he averred, can however be managed with proper safeguards, ethical oversight, and user education.
Scoring current efforts of the Federal Government through the Federal Ministry of Communications, Innovation and Digital Economy, and an agency such as the National Information Technology Development Agency (NITDA) in the area of domesticating emerging technologies such AI, robotics, big data etc, Adeniyi said the government is making great efforts.
“I follow Dr. Bosun Tijani on LinkedIn and often see his updates on several fascinating programs like the Llama Impact Accelerator, launched in partnership with Meta. It supports Nigerian innovators with funding and technical training.
“However, there’s still room to grow, especially in building national AI infrastructure; expanding AI education in schools and universities; encouraging collaboration between government, private sector, and academia; and making sure rural communities are included.
“The vision is promising, but we need faster execution and wider reach to make real impact,” Adeniyi said.
