Multimodal LLM Patterns

Integrate vision, audio, and video generation capabilities from leading multimodal models. Covers image analysis, document understanding, real-time voice agents, speech-to-text, text-to-speech, and AI video generation (Kling 3.0, Sora 2, Veo 3.1, Runway Gen-4.5).

Quick Reference

Category	Rules	Impact	When to Use
Vision: Image Analysis	1	HIGH	Image captioning, VQA, multi-image comparison, object detection
Vision: Document Understanding	1	HIGH	OCR, chart/diagram analysis, PDF processing, table extraction
Vision: Model Selection	1	MEDIUM	Choosing provider, cost optimization, image size limits
Audio: Speech-to-Text	1	HIGH	Transcription, speaker diarization, long-form audio
Audio: Text-to-Speech	1	MEDIUM	Voice synthesis, expressive TTS, multi-speaker dialogue
Audio: Model Selection	1	MEDIUM	Real-time voice agents, provider comparison, pricing
Video: Model Selection	1	HIGH	Choosing video gen provider (Kling, Sora, Veo, Runway)
Video: API Patterns	1	HIGH	Async task polling, SDK integration, webhook callbacks
Video: Multi-Shot	1	HIGH	Storyboarding, character elements, scene consistency

Total: 9 rules across 3 categories (Vision, Audio, Video Generation)

Vision: Image Analysis

Send images to multimodal LLMs for captioning, visual QA, and object detection. Always set max_tokens and resize images before encoding.

Rule	File	Key Pattern
Image Analysis	rules/vision-image-analysis.md	Base64 encoding, multi-image, bounding boxes

Vision: Document Understanding

Extract structured data from documents, charts, and PDFs using vision models.

Rule	File	Key Pattern
Document Vision	rules/vision-document.md	PDF page ranges, detail levels, OCR strategies

Vision: Model Selection

Choose the right vision provider based on accuracy, cost, and context window needs.

Rule	File	Key Pattern
Vision Models	rules/vision-models.md	Provider comparison, token costs, image limits

Audio: Speech-to-Text

Convert audio to text with speaker diarization, timestamps, and sentiment analysis.

Rule	File	Key Pattern
Speech-to-Text	rules/audio-speech-to-text.md	Gemini long-form, GPT-4o-Transcribe, AssemblyAI features

Audio: Text-to-Speech

Generate natural speech from text with voice selection and expressive cues.

Rule	File	Key Pattern
Text-to-Speech	rules/audio-text-to-speech.md	Gemini TTS, voice config, auditory cues

Audio: Model Selection

Select the right audio/voice provider for real-time, transcription, or TTS use cases.

Rule	File	Key Pattern
Audio Models	rules/audio-models.md	Real-time voice comparison, STT benchmarks, pricing

Video: Model Selection

Choose the right video generation provider based on use case, duration, and budget.

Rule	File	Key Pattern
Video Models	rules/video-generation-models.md	Kling vs Sora vs Veo vs Runway, pricing, capabilities

Video: API Patterns

Integrate video generation APIs with proper async polling, SDKs, and webhook callbacks.

Rule	File	Key Pattern
API Integration	rules/video-generation-patterns.md	Kling REST, fal.ai SDK, Vercel AI SDK, task polling

Video: Multi-Shot

Generate multi-scene videos with consistent characters using storyboarding and character elements.

Rule	File	Key Pattern
Multi-Shot	rules/video-multi-shot.md	Kling 3.0 character elements, 6-shot storyboards, identity binding

Key Decisions

Decision	Recommendation
High accuracy vision	Claude Opus 4.6 or GPT-5
Long documents	Gemini 2.5 Pro (1M context)
Cost-efficient vision	Gemini 2.5 Flash ($0.15/M tokens)
Video analysis	Gemini 2.5/3 Pro (native video)
Voice assistant	Grok Voice Agent (fastest, <1s)
Emotional voice AI	Gemini Live API
Long audio transcription	Gemini 2.5 Pro (9.5hr)
Speaker diarization	AssemblyAI or Gemini
Self-hosted STT	Whisper Large V3
Character-consistent video	Kling 3.0 (Character Elements 3.0)
Narrative video / storytelling	Sora 2 (best cause-and-effect coherence)
Cinematic B-roll	Veo 3.1 (camera control + polished motion)
Professional VFX	Runway Gen-4.5 (Act-Two motion transfer)
High-volume social video	Kling 3.0 Standard ($0.20/video)
Open-source video gen	Wan 2.6 or LTX-2
Lip-sync / avatar video	Kling 3.0 (native lip-sync API)

Example

import anthropic, base64

client = anthropic.Anthropic()
with open("image.png", "rb") as f:
    b64 = base64.standard_b64encode(f.read()).decode("utf-8")

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": [
        {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": b64}},
        {"type": "text", "text": "Describe this image"}
    ]}]
)

Common Mistakes

1. Not setting max_tokens on vision requests (responses truncated)
2. Sending oversized images without resizing (>2048px)
3. Using high detail level for simple yes/no classification
4. Using STT+LLM+TTS pipeline instead of native speech-to-speech
5. Not leveraging barge-in support for natural voice conversations
6. Using deprecated models (GPT-4V, Whisper-1)
7. Ignoring rate limits on vision and audio endpoints
8. Calling video generation APIs synchronously (they're async — poll or use callbacks)
9. Generating separate clips without character elements (characters look different each time)
10. Using Sora for high-volume social content (expensive, slow — use Kling Standard instead)

Related Skills

• ork:rag-retrieval - Multimodal RAG with image + text retrieval
• ork:llm-integration - General LLM function calling patterns
• streaming-api-patterns - WebSocket patterns for real-time audio
• ork:demo-producer - Terminal demo videos (VHS, asciinema) — not AI video gen