Multimodal LLM Patterns

Integrate vision and audio capabilities from leading multimodal models. Covers image analysis, document understanding, real-time voice agents, speech-to-text, and text-to-speech.

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

Total: 6 rules across 2 categories (Vision, Audio)

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

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

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

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