Multimodal AI brings text, images, audio and video into a shared semantic space. With cross‑modal attention, models align signals across modalities to understand complex scenes and generate outputs that better match intent.

Why it matters

• Assistants can see images you upload and answer questions.
• Creative tools generate images/videos from text prompts.
• Search engines reason over screenshots, diagrams, and text together.

Common tasks

• VQA (Visual Question Answering)
• Image captioning and grounding
• Text-to-image and text-to-video generation
• Document understanding (charts, tables + text)

How models fuse modalities

• Shared embeddings: map images/text/audio into a common vector space for retrieval and alignment.
• Cross‑modal attention: let text attend to image regions (and vice versa) to control “where to look.”
• Instruction tuning: train on image‑text/video‑text instruction pairs so the model follows natural language instructions.

Practical generation checklist

• Choose aspect ratio first: 1:1, 3:4, 16:9 impact composition; the wrong ratio wastes iterations.
• Separate factors: list subject/style/lighting/camera/material/details/palette/constraints on separate lines.
• Use 1–2 examples: a couple of references align style better than long adjective piles.
• Coarse‑to‑fine: draft small images to pick composition, then upscale and refine; avoid jumping straight to ultra‑high resolution.

Evaluation and comparison

• Subjective: prompt fit/art style/composition/noise/detail completeness.
• Objective: resolution/human‑body proportion error/text fidelity (for text rendering).
• Comparison: generate 3–5 outputs across models on the same prompt and pick via blind review.

Further reading

Prompt Engineering · Evals & Launch Gates · Transformer Explained