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Vision-Language Models (API Usage)

How vision-language models work — just enough to use them in RAG pipelines and multimodal apps

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▶📚 Prerequisites(2)

🎓 Learning objectives

  • •Explain how CLIP aligns image and text embeddings via contrastive learning
  • •Describe how images are tokenized into patches for ViT-based models
  • •Call GPT-4V/Claude/Gemini with image inputs via API
  • •Build a simple visual Q&A pipeline
  • •Understand the difference between image embeddings and text embeddings

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📂 Subtopics

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How Vision-Language Models Work: Encoders, Decoders, and CLIP's Shared Embedding Space

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Key Architectures: CLIP, LLaVA, and GPT-4V/Gemini Vision

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Common Tasks: Image Captioning, Visual QA, Document Understanding, and OCR

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Using VLMs in Practice: API Usage and Prompt Design for Images

Related concepts

embeddings-basicsrag-workflowmultimodal-rag-pipeline

Next to learn

multimodal-rag-pipeline →
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