Building multimodal applications requires careful attention to data handling, cost management, and user experience design.

Architecture Patterns

Pattern 1: Multimodal RAG

1. Ingest documents with mixed content (text, images, tables)
2. Use a VLM to describe images and extract table data as text
3. Embed all content (including image descriptions) in a vector store
4. Retrieve and present with original images intact

Pattern 2: Multimodal Agent

1. Agent receives user input (text + optional images/audio)
2. Plans which tools to use (search, code execution, image generation)
3. Executes tools and synthesizes results
4. Returns response in the most appropriate modality

Pattern 3: Pipeline Processing

1. Audio → Whisper → Text transcript
2. Text → LLM → Structured data + summary
3. Structured data → Charts/visualizations
4. All outputs → Combined report

Cost Management

Multimodal inputs are expensive:

• Image tokens cost 2-10× more than text tokens
• Video processing can cost $1+ per minute
• Audio transcription is relatively cheap (~$0.006/minute with Whisper)

Optimization strategies:

• Resize images to the minimum resolution needed (many tasks work fine at 512px)
• Extract key frames from video instead of processing every frame
• Cache multimodal embeddings aggressively
• Use smaller models for classification/routing, larger models for complex analysis

User Experience Design

• Progressive disclosure: Show text summary immediately, load detailed visual analysis in the background
• Modality selection: Let users choose their preferred input method (type, speak, upload)
• Graceful degradation: If image processing fails, fall back to text-only analysis
• Feedback loops: Let users correct multimodal extraction errors to improve future accuracy

Testing Multimodal Systems

Build test suites with diverse inputs:

• Various image formats, resolutions, and orientations
• Handwritten vs. printed text
• Charts with different styles and complexity levels
• Audio with background noise, accents, and multiple speakers
• Edge cases: blank images, corrupted files, very long audio

Automate quality checks but include regular human evaluation for nuanced quality assessment.