Getting Started with AI-Generated Content
How I think about a first AIGC pipeline — prompts, review, publish — and how that path led me to join Elser AI.
What is AIGC?
AI-Generated Content (AIGC) refers to content — text, images, code, video — produced with the assistance of generative AI models. I started out like many builders: experimenting on nights and weekends, trying to turn prompts into repeatable pipelines instead of one-off demos.
The Stack I Use
For text generation I rely on the OpenAI API with custom system prompts tuned to my writing style. For images, I've been experimenting with Stable Diffusion and Flux. (At work, the exact stack evolves — the important part is the same loop: prompt → model → evaluate → ship.)
Practical Workflow
- Input: A topic or outline
- Generation: LLM draft with structured prompts
- Review: Human-in-the-loop editing
- Publish: Automated deployment via GitHub Actions
This loop lets you produce high-quality output at scale without sacrificing judgement — whether you're building solo or on a product team.
From experiments to Elser AI
Those building blocks — prompt design, evaluation, and shipping real workflows — are what pulled me toward Elser AI. I joined the company to work on generative AI in a serious product context: not only research demos, but systems that have to hold up for real users.
This post is still the mental model I use when I think about AIGC: start small, close the loop, then scale what actually works.
What's Next
Keep tightening that loop — in product at Elser AI and in public experiments here. If you're early on the same path, nail review and measurement before you automate; that's what turns a cool prompt into a reliable pipeline.