Picture by Creator | Canva
# Introduction
That is the second article in my newbie venture collection. When you haven’t seen the primary one on Python, it’s price testing: 5 Enjoyable Python Tasks for Absolute Newbies.
So, what’s generative AI or Gen AI? It’s all about creating new content material like textual content, photographs, code, audio, and even video utilizing AI. Earlier than the big language and imaginative and prescient fashions period, issues have been fairly completely different. However now, with the rise of basis fashions like GPT, LLaMA, and LLaVA, the whole lot has shifted. You possibly can construct inventive instruments and interactive apps with out having to coach fashions from scratch.
I’ve picked these 5 tasks to cowl a little bit of the whole lot: textual content, picture, voice, imaginative and prescient, and a few backend ideas like fine-tuning and RAG. You’ll get to check out each API-based options and native setups, and by the tip, you’ll have touched all of the constructing blocks utilized in most fashionable Gen AI apps. So, Let’s get began.
# 1. Recipe Generator App (Textual content Technology)
Hyperlink: Construct a Recipe Generator with React and AI: Code Meets Kitchen
We’ll begin with one thing easy and enjoyable that solely makes use of textual content era and an API key, no want for heavy setup. This app helps you to enter a couple of primary particulars like components, meal sort, delicacies choice, cooking time, and complexity. It then generates a full recipe utilizing GPT. You’ll learn to create the frontend kind, ship the info to GPT, and render the AI-generated recipe again to the person. Right here is one other superior model of similar concept: Create an AI Recipe Finder with GPT o1-preview in 1 Hour. This one has extra superior immediate engineering, GPT-4, options, ingredient substitutions, and a extra dynamic frontend.
# 2. Picture Generator App (Steady Diffusion, Native Setup)
Hyperlink: Construct a Python AI Picture Generator in 15 Minutes (Free & Native)
Sure, you’ll be able to generate cool photographs utilizing instruments like ChatGPT, DALL·E, or Midjourney by simply typing a immediate. However what if you wish to take it a step additional and run the whole lot regionally with no API prices or cloud restrictions? This venture does precisely that. On this video, you’ll learn to arrange Steady Diffusion by yourself laptop. The creator retains it tremendous easy: you put in Python, clone a light-weight internet UI repo, obtain the mannequin checkpoint, and run an area server. That’s it. After that, you’ll be able to enter textual content prompts in your browser and generate AI photographs immediately, all with out web or API calls.
# 3. Medical Chatbot with Voice + Imaginative and prescient + Textual content
Hyperlink: Construct an AI Voice Assistant App utilizing Multimodal LLM Llava and Whisper
This venture isn’t particularly constructed as a medical chatbot, however the use case suits effectively. You converse to it, it listens, it may well have a look at a picture (like an X-ray or doc), and it responds intelligently combining all three modes: voice, imaginative and prescient, and textual content. It’s constructed utilizing LLaVA (a multimodal vision-language mannequin) and Whisper (OpenAI’s speech-to-text mannequin) in a Gradio interface. The video walks by way of setting it up on Colab, putting in libraries, quantizing LLaVA to run in your GPU, and stitching all of it along with gTTS for audio replies.
# 4. High-quality-Tuning Fashionable LLMs
Hyperlink: High-quality tune Gemma 3, Qwen3, Llama 4, Phi 4 and Mistral Small with Unsloth and Transformers
To this point, we’ve been utilizing off-the-shelf fashions with immediate engineering. That works, however if you need extra management, fine-tuning is the following step. This video from Trelis Analysis is without doubt one of the greatest on the market. Due to this fact, as a substitute of suggesting a venture that merely swaps a fine-tune mannequin, I wished you to focuse on the precise means of fine-tuning a mannequin your self. This video exhibits you easy methods to fine-tune fashions like Gemma 3, Qwen3, Llama 4, Phi 4, and Mistral Small utilizing Unsloth (library for quicker, memory-efficient coaching) and Transformers. It’s lengthy (about 1.5 hours), however tremendous price it. You’ll be taught when fine-tuning is smart, easy methods to prep datasets, run fast evals utilizing vLLM, and debug actual coaching points.
# 5. Construct Native RAG from Scratch
Hyperlink: Native Retrieval Augmented Technology (RAG) from Scratch (step-by-step tutorial)
Everybody loves a great chatbot, however most crumble when requested about stuff exterior their coaching knowledge. That’s the place RAG is helpful. You give your LLM a vector database of related paperwork, and it pulls context earlier than answering. The video walks you thru constructing a completely native RAG system utilizing a Colab pocket book or your personal machine. You’ll load paperwork (like a textbook PDF), break up them into chunks, generate embeddings with a sentence-transformer mannequin, retailer them in SQLite-VSS, and join all of it to an area LLM (e.g. Llama 2 by way of Ollama). It’s the clearest RAG tutorial I’ve seen for newbies, and when you’ve achieved this, you’ll perceive how ChatGPT plugins, AI search instruments, and inside firm chatbots actually work.
# Wrapping Up
Every of those tasks teaches you one thing important:
Textual content → Picture → Voice → High-quality-tuning → Retrieval
When you’re simply entering into Gen AI and wish to really construct stuff, not simply play with demos, that is your blueprint. Begin from the one which excites you most. And keep in mind, it is okay to interrupt issues. That’s the way you be taught.
Kanwal Mehreen Kanwal is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with drugs. She co-authored the book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and tutorial excellence. She’s additionally acknowledged as a Teradata Variety in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.
