✓ Accepted Answer
Here is the most practical way I know to approach generation:
**Step 1 — Understand what you actually need from generation.** Most people skip this and spend time solving the wrong problem. Write down your specific goal in one sentence.
**Step 2 — Survey the landscape.** Look at 4 real examples of diffusion being handled well. You will notice patterns across them that tell you which approach fits your situation.
**Step 3 — Start with the minimum working version.** Do not build the complete solution first. Validate that the core idea works in your context.
**Step 4 — Test under real conditions.** Real usage always surfaces something the examples didn't cover.
**Step 5 — Iterate.** The first version is rarely the right version — plan for 4 refinement cycles.
AI outputs should be treated as a starting point requiring human review, not a finished product.
The part most people underestimate with generation: the edge cases accumulate quickly once you're past the basic examples.
by chloeleclerc10103