AI Agent Engineer

AI Agent Engineer
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Image Generation Learning Path

4 min read

Image Generation

About this knowledge base

Image generation creates new pixels from text, an existing image, or a mask. This course does not treat “write one prompt” as complete engineering. You first understand tasks and generation principles, then organize prompts, input rights, quality criteria, human review, cost, and provenance into an auditable workflow. Every input and output also needs a traceable version, object-level access control, and an executable revocation/deletion propagation plan. The course neither requires a local GPU nor downloads models or generates images.

Where this course fits in the overall route

It belongs to the Extended Applications and Complex Collaboration stage. Earlier LLM and Agent capabilities interpret needs, call tools, and manage state; this course turns a visual task into a constrained, acceptable generation job.

Learning objectives

  • Distinguish text-to-image, image-to-image, local editing, outpainting, and variations.
  • Explain autoregressive and diffusion generation intuitively, and know why randomness does not mean it cannot be managed.
  • Express subject, composition, style, text, edit constraints, and acceptance criteria as a structured task.
  • Design provider-neutral API boundaries, quality review, safety review, and reproduction records; distinguish source_revision, transform_id, and release_id.
  • Run an offline auditor that finds missing rights, risk, and acceptance information before calling a model.

Prerequisites

Complete Prompt Engineering, Deep Learning, and JSON if possible. You can begin without familiarity with matrices or probability; the course explains terms such as “latent variable” and “sampling” when first used.

  1. Task types and image representations — first define inputs, outputs, and pixel representation.
  2. Diffusion and autoregressive intuition — build a minimum mental model of two common generation routes.
  3. Conditioning and prompt design — turn a natural-language need into checkable constraints.
  4. Composition, style, text, and editing — handle the most common visual-control challenges.
  5. Workflows and API boundaries — isolate provider change and manage asynchronous jobs.
  6. Quality evaluation and human review — break “looks good” into actionable review dimensions.
  7. Safety, copyright, and Content Credentials — retain rights and provenance evidence before and after generation.
  8. Cost, reproducibility, and troubleshooting — limit attempts and retain replayable information.
  9. Project: Image Generation Task Audit — audit a generation plan without calling a model.

Hands-on project

Begin with the offline project guide, inspect the example task manifest, then run the task auditor, basic behavior tests, and contract and CLI regression tests. The project reads JSON only and produces no media files.

Mastery checklist

  • Can select generation, editing, inpainting, or outpainting from the requirement instead of only saying “make an image.”
  • Can explain why prompt adherence, visual quality, text correctness, and rights compliance need separate acceptance.
  • Can write a task manifest containing input provenance, risk, budget, reproduction, and human review.
  • Can state the applicability boundary of automated metrics and design blind review or paired comparison.
  • Can design a generation interface without exposing keys or hard-coding provider parameters.
  • Can enforce object-level authorization/ACL before candidate scoring and invalidate derived output, caches, and links as planned on revocation or deletion.
  • Can explain that Content Credentials provide verifiable provenance claims only; a factual publication must meet evidence_supported and cannot be supported by a generated image alone.
  • The offline auditor and its tests pass.

Relationship to other knowledge bases

Primary references

Sources were checked on 2026-07-22. Provider interfaces, model names, limits, and prices change; recheck official documentation before a real integration.

Backlinks