Multimodal AI · TRACK 01/04
Vision & Document Understanding
How models read images, screenshots, documents, and video.
// THE TRACK
01 · START HEREVision Language Models (VLMs)Understand what a vision language model is, how an LLM gains sight, and what tasks VLMs unlock — from screenshot Q&A to chart reading.BEGINNERVision Models for Document AILearn how to point GPT, Claude, or Gemini at a document and get structured data out — and when that beats a traditional OCR pipeline.INTERMEDIATEImage Tokens & PatchesLearn the path from pixels to tokens — patches, vision encoders, and projection layers — so vision model behavior stops feeling like magic.BEGINNERSending Images to an APIWalk away able to send your first image to a vision API — encoding options, size limits, and working request examples for the big three providers.BEGINNERMultimodal AI BasicsGet the foundation for the whole multimodal world: what it means for one model to handle text, images, and audio, and how the modalities fit together.BEGINNERImage Token CostUnderstand exactly how providers convert image dimensions into billable tokens, and learn resizing tricks that cut vision costs without hurting quality.INTERMEDIATELLM OCR vs TesseractKnow when a vision LLM beats traditional OCR, how document understanding actually works, and how to extract structured data from messy scans.INTERMEDIATESending PDFs to an APILearn every way to get a PDF into a model — native document inputs, Files APIs, and text extraction — and when each one is the right call.INTERMEDIATEVLM vs Multimodal LLMUntangle the overlapping terms VLM, multimodal LLM, and omni-model so you know exactly what each one promises.BEGINNERExtracting Tables from ImagesLearn the prompting and schema patterns that turn a screenshot of a table into reliable structured JSON or CSV.INTERMEDIATEVision Model LimitationsKnow the predictable ways vision models fail — counting, tiny text, spatial reasoning — so you can design around them before they bite.INTERMEDIATEQwen-VLYou will understand what Qwen-VL is, how an open vision-language model reads images and video, and why it became a default open VLM family.INTERMEDIATELLaVAYou will understand what LLaVA was, how visual instruction tuning connects a vision encoder to an LLM, and why this origin story still shapes today's VLMs.INTERMEDIATEFlorence-2You will understand what Florence-2 is, how one small model handles captioning, detection, OCR, and segmentation through prompts, and why that unification matters.INTERMEDIATEPaddleOCRYou will understand what PaddleOCR is, how its detection-plus-recognition pipeline reads text from images, and why it is a default free OCR choice at scale.INTERMEDIATEColPaliYou will understand what ColPali is, how it retrieves documents by embedding page images instead of parsed text, and why this defines the vision RAG trend.ADVANCED