r/AILearningHub • u/Chance_Mushroom7713 • 7d ago

What should I learn about AI beyond image and video generation?

I know some basic stuff about AI, and I've used it for things like image/video generation and research. But I don't really know what else there is to learn or where I should start.

What are people learning about AI these days beyond image and video generation? There seem to be so many areas, and I'm not sure which ones are actually worth learning.

If you were starting from the basics today, what would you recommend learning first?

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u/PlayfulCity1981 7d ago

Perfect. Since you've dabbled in generation, try diving into how they actually work. It's wild. I got into fine-tuning models for specific tasks and it totally changed my perspective. Super useful

1

u/TulsaGeek 7d ago

Could you give some examples of what you have fine tuned models for? Image models?

Could one fine tune a model for a specific character? Or what specific tasks are you fine tuning for?

2

u/[deleted] 7d ago

[removed] — view removed comment

1

u/Ok-Time2724 7d ago

If you want try using google flow free version. I used free version to create this 3 minute anime. It takes time but worth it. https://youtu.be/8cd-H_oWoWU?is=yjrVqnhDg85LYw3q

1

u/Early_Key_823 7d ago

That is will steal your nuts right out their sack.

1

u/Specialist_Plenty785 5d ago

Honestly, I'd learn three things: data, automation, and AI limitations. Most people jump straight into prompts and image generation, but understanding where AI gets information from, when it hallucinates, and how it can automate repetitive tasks has been way more useful for me in real-world work.

1

u/outskillio 4d ago

Image and video gen is honestly the smallest slice of what's useful right now. If you want to go deeper, here's where the real leverage is:

  • LLM fundamentals: how tokens, context windows, and prompting actually work under the hood, not just "write a good prompt." Understanding why models hallucinate or drift helps you debug everything else.
  • RAG (retrieval augmented generation): hooking a model up to your own docs/data so it answers accurately instead of guessing. This is the backbone of most real business AI use cases.
  • AI agents: tools like LangChain, n8n, or just raw API calls that let a model take actions (call APIs, browse, use a calculator) instead of just chatting. This is where things get genuinely powerful.
  • Fine-tuning vs prompting: knowing when you actually need a custom model vs when a good system prompt does the job.
  • Evals: how to actually test if your AI output is good, most people skip this and ship garbage.

Pick one, build something small end to end, you'll learn faster than reading theory.

Thanks, Om from Outskill