What is Generative AI?
Generative AI is artificial intelligence that can create new content rather than simply analyzing or processing existing information. Unlike traditional AI that classifies, predicts, or recommends based on training data, generative AI learns patterns from vast datasets and then produces original text, images, code, audio, or video. Built on foundation models like GPT, DALL-E, or Stable Diffusion, these systems can write essays, generate artwork, compose music, write software code, and create countless other types of content—making them powerful tools for anyone wanting to build AI-powered applications.
If you are an engineer eager to get started on building Generative AI applications, the current information overload and hype cycle currently associated with Generative AI could get overwhelming for many.
Here is a list of topics that helped me get started and enjoy building Generative Ai apps.
Large and Small Language Models
Majority of the people I’ve interacted with think Generative AI = LLMs, and that one needs to go deep into understanding LLMs and how they work and how to go about training & fine tuning LLMs etc. The truth is LLMs are just one of the many components (albite and important one) of a any Generative AI application. Things have evolved to such an extent that LLMs have sort of become a commodity and many of them are available as SaaS/PaaS which are exposed as APIs such that one can simply build a React based frontend app on top of an LLM. OpenAI APIs are a classic example.
To play around with a bunch of self-hosted and local LLMs, install tools like Ollama or LM Studio where you can easily download most of the models from Llama2 to Mistral to hugginface and try them out. While Ollama provides a CLI based endpoint that you can hit to talk to an LLM, LM Studio provides a nifty chat UI interface where you cah test out the different LLMs.
Frontend UIs
If you are a frontend engineer or are looking to build a frontend app that talks to your LLMs then I would highly recommend the Vercel AI SDK and the Vercel AI templates, that allow you to clone and deploy a Gen AI app within minuties.
Prompt Engineering
Yup I know prompt engineering is a beaten horse, and people dont talk a lot about it now as compared to about 6 months back, but I can’t emphasise enough the importance of prompt engineering and the impact it has on the quality of the outputs of your Gen Ai application. Remember that with Gen AI you are building probablistic applications and the probablity of how good the apps respond to a user’s input heavily relies on the context that is passed to the LLMs via the prompt template and the prompts. All of which falls under the umbrella of Prompt Engineering.
Langchain
Over 95% of all Gen AI app tutorials that you come across would use langchain in some shape or form, and I would highly recommend investing a decent amount of time understanding langchain, you can choose either the python version or the JavaScript version of Langchain. While working with Langchain make sure you undesteand the concepts around, chains, tools, agents etc.
An Agent framework
As you move into advanced stages of building apps you’ll come across the notion of agents and multi-agent converstions, and this where there are a bunch of tools you can start looking into. Here is just a small collection of agent frameworks that have worked well for me:
Semantic Kernel
Autogen
Taskweaver
CrewAI
Common Apps to Build when learning GenAI
There is no dearth of GenAI tutorials on the internet and YouTube, and most of them are really good to get started. However, if the information overload is overwhelming, then here are a few apps to try out.
RAG based chat over a document
One of the most popular applications to build is a chatbot that answers questions about a document (usually a PDF) or a collection of documents. This is called a RAG Application, where RAG stands for Retrieval Augmented Generation. Here, you learn some key concepts on how to create chunks and embeddings from an unstructured document and store them in a Vector database. Then, you learn to query the vector database to retrieve chunks that closest match the question asked and pass it through an LLM (Large Language Model) to get a natural language output. It’s an excellent way to understand the integration of retrieval systems with generative AI.
Build a CustomGPT
OpenAI recently launched the ability to create a CustomGPT that is ‘finetuned’ or ‘trained’ to answer questions on a specific set of topics. This is a good starting point for building a no-code GenAI application. With CustomGPT, you can specialize the model on a niche subject area or style, making it ideal for creating unique applications or enhancing existing services with a tailored conversational agent. The process involves selecting relevant training data and iteratively refining the model’s responses, providing a hands-on experience with model training and fine-tuning.
Generate Artistic Images
Another exciting project is to build an application that generates artistic images based on textual descriptions using Generative AI models. This involves understanding and utilizing models like DALL-E or similar technologies. Such a project would not only be fun but also educational in terms of understanding how AI can interpret and visualize creative prompts. It’s a great way to explore the intersection of AI and creativity.
Automated Content Creation
A practical and increasingly popular GenAI application is automated content creation. This could range from generating blog articles, creating marketing copy, to even drafting code. Such applications provide an insight into how AI can assist in repetitive or creative tasks, enhancing productivity and sparking new ideas.
Personalized Recommendation Systems
Finally, building a personalized recommendation system using GenAI can be both challenging and rewarding. Such systems could recommend products, content, or even actions based on user behavior and preferences. It encompasses learning about user profiling, behavior analysis, and the application of AI in making predictive decisions.
In summary, starting with these projects can provide a comprehensive and engaging way to dive into the world of Generative AI, offering both foundational learning and practical experience.
FAQ
Do I need deep machine learning knowledge to work with Generative AI?
Not necessarily. While understanding ML concepts helps, many Generative AI applications are built using pre-trained models accessed through APIs. Modern frameworks like Vercel AI SDK, LangChain, and others abstract away much of the complexity. You can build powerful Gen AI applications with general programming skills, API knowledge, and understanding of prompt engineering—learning ML deeper as you advance to more sophisticated applications.
What’s the difference between LLMs and other types of Generative AI?
Large Language Models (LLMs) like GPT, Claude, and Llama are specifically designed for text generation and understanding—they process and generate human language. Other Generative AI models specialize in different modalities: image models (DALL-E, Midjourney, Stable Diffusion) create visuals, audio models (ElevenLabs, Whisper) generate or process sound, and code models (Copilot, CodeLlama) produce programming code. LLMs are just one component of the broader Generative AI ecosystem.
Should I use cloud APIs or self-host models for my projects?
Start with cloud APIs like OpenAI or Anthropic for faster development and better performance—they handle infrastructure, scaling, and model updates. Self-hosting with tools like Ollama or LM Studio makes sense when you need data privacy, lower latency, cost control at scale, or offline capabilities. Many projects start with APIs and later migrate to self-hosted models as requirements evolve. Choose based on your specific needs for privacy, cost, performance, and control.
What is prompt engineering and why does it matter?
Prompt engineering is the craft of designing effective inputs (prompts) that guide AI models to produce desired outputs. Because Gen AI applications are probabilistic—their output depends heavily on the context they receive—the quality of your prompts dramatically affects results. Good prompt engineering includes writing clear instructions, providing relevant context, specifying output formats, using examples (few-shot learning), and iterating based on results. It’s often the difference between mediocre and excellent AI performance.
What’s the role of vector databases in Gen AI applications?
Vector databases store and search embeddings—numeric representations of text, images, or other data that capture semantic meaning. They’re essential for RAG (Retrieval Augmented Generation) applications where you need to find relevant information to provide as context to an LLM. When a user asks a question, you search the vector database for similar content, retrieve those passages, and include them in the prompt—enabling the AI to answer questions using your specific data rather than just its training knowledge.
What are AI agents and how do they differ from simple prompts?
AI agents are systems that can plan, reason, and execute multi-step tasks autonomously, while simple prompts are one-shot requests. Agents use LLMs to decide what actions to take, can use tools (search APIs, code execution, databases), learn from feedback, and iterate on their approach. Frameworks like LangChain agents, AutoGen, and CrewAI help build multi-agent systems that collaborate on complex tasks—moving beyond single prompt-response to more sophisticated, autonomous workflows.
What are some beginner-friendly Gen AI projects to build?
Great starter projects include: RAG-based chat over documents (PDFs, websites), which teaches embeddings, vector databases, and context management; AI writing assistants for emails, reports, or creative content; image generation apps that turn text descriptions into visuals; code explanation and generation tools; and simple chatbots for specific domains. Each teaches different aspects of Gen AI development while producing something tangible and useful.
How long does it take to get productive with Gen AI development?
Most developers can build basic Gen AI applications within a few weeks of focused learning. Start with simple API-based projects, gradually adding complexity like RAG, agents, or fine-tuning. Expect 1-2 months to become comfortable with core concepts (prompting, context management, basic architectures) and 3-6 months to build more sophisticated applications. The learning curve is front-heavy—once you understand fundamentals, advancing becomes faster. Focus on building real projects rather than just studying concepts.
About the Author
Vinci Rufus is a technology practitioner who helps engineers and developers navigate the rapidly evolving landscape of Generative AI. Passionate about cutting through hype to practical learning paths, he writes about getting started with AI, building real applications, and developing the skills needed for the AI-powered future of software development.