{"slug": "how-does-ai-know-what-s-inside-an-image-meet-vision-language-models", "title": "How Does AI Know What's Inside an Image? Meet Vision Language Models", "summary": "Rijul, an engineer building LiveReview, explains how Vision-Language Models (VLMs) process images by combining a vision encoder, a connector, and a language model. The post breaks down the pipeline from image patches to visual representations, and distinguishes perception and grounding as key capabilities.", "body_md": "*Hello, I'm Rijul, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product.*\n\nDo you interact with AI models where you put in an image and then it processes it?\n\nDo you wonder what these models are and what is happening behind the scenes?\n\nThis is called a **VLM**, or **Vision-Language Model**.\n\nA VLM combines vision and language capabilities so that it can process visual information along with text.\n\nA simple way to think about it is:\n\n**LLM → Works primarily with text**\n\n**VLM → Works with images and text**\n\nThis is a simplified mental model, since VLM architectures can vary. Under the hood, a VLM often combines a **vision encoder**, a **connector or projector**, and a **language model**.\n\nSo, how does a VLM actually \"see\" an image?\n\nAn LLM works with tokens.\n\nBut an image is not naturally made up of language tokens.\n\nSo we need a way to convert the visual information in an image into something the language model can work with.\n\nA simplified view looks like this:\n\n```\nImage\n  ↓\nVision Encoder\n  ↓\nVisual Representations\n  ↓\nVision-Language Connector\n  ↓\nLanguage Model\n  ↓\nText Response\n```\n\nLet's break this down.\n\nThe first important component is the **Vision Encoder**.\n\nIts job is to process the image and turn the visual information into numerical representations that the rest of the model can work with.\n\nDepending on the vision architecture, the image may be divided into smaller regions or **patches**.\n\nFor example:\n\nEach part is processed by the vision encoder, which produces **visual representations**.\n\nThese representations don't simply mean \"this patch is a dog\" or \"this patch is blue.\"\n\nThe vision encoder builds representations that capture visual patterns and context, which can help the model understand things such as shapes, objects, colors, text, spatial relationships, and other visual information.\n\nThe exact way this happens depends on the vision encoder architecture. Patch-based processing is common, but it is not universal to every vision system.\n\nThe result is essentially a representation of the image that can be passed toward the language model.\n\nNow that we have visual representations, we can talk about two important capabilities: **perception** and **grounding**.\n\nPerception is about understanding the visual content.\n\nIt is not just:\n\n\"What objects are in this image?\"\n\nIt can also involve understanding relationships between objects, actions, scenes, and the overall visual context.\n\nFor example, given an image of a person riding a bicycle, the model may understand:\n\n```\nPerson\n  ↓\nriding\n  ↓\nBicycle\n```\n\nSo perception is about building an understanding of what is happening in the image.\n\nGrounding is about connecting language to specific parts of the visual input.\n\nFor example, if we ask:\n\n\"Where is the dog?\"\n\nThe model could associate the word **dog** with a particular region of the image.\n\nA simplified representation could be:\n\n```\n{\n  \"object\": \"dog\",\n  \"location\": \"center of the image\"\n}\n```\n\nIn more technical systems, grounding can involve associating language with **regions, objects, points, or other parts of an image**. Bounding boxes are one common way of representing this, but they are not the only form of grounding.\n\nThis becomes especially useful when we want an AI system to not only identify something, but also connect that understanding to **where or what it refers to in the image**.\n\nYou may also hear another term:\n\n**Multimodal LLM**\n\nSo what is the difference?\n\nThe terminology isn't perfectly standardized, but a useful way to think about it is:\n\n**VLM** usually emphasizes models that connect **vision and language**.\n\nExamples include models such as:\n\n**Multimodal LLM** is a broader term often used for language models that can work with multiple types of inputs or outputs, such as:\n\n```\nText\nImages\nAudio\nVideo\n```\n\nSo, conceptually, you can think of VLMs as one part of the broader multimodal AI space.\n\nThe exact terminology can vary depending on the model and how its creators describe it, so these categories should be treated as a useful mental model rather than strict technical boundaries.\n\nSo, the basic idea behind a VLM is:\n\n```\nImage\n  ↓\nVision Encoder\n  ↓\nVisual Representations\n  ↓\nConnector / Projector\n  ↓\nLanguage Model\n  ↓\nResponse\n```\n\nThe vision side processes the image and produces visual representations.\n\nThe connector helps make those representations usable by the language model.\n\nThe language model then uses those visual representations together with the text input to generate a response.\n\nVLMs become especially useful when text alone isn't enough to express what we want an AI system to understand.\n\nFor example, instead of describing a UI design, diagram, screenshot, chart, or photograph entirely through text, we can simply give the model the image.\n\nIn that sense, VLMs give AI systems a way to work with visual information alongside language, making it possible to interact with information that previously had to be described using words.\n\nSo that's it about this article, see you on the next one!\n\nYour team's attention is limited, and the deluge of AI-generated code is making it harder to keep production stable while also shipping at high velocity.\n\nI'm building **LiveReview**, a blast-radius aware AI code review built for your business-critical systems.\n\nInstead of presenting every diff with equal emphasis, **LiveReview scores each change by blast radius — how far its impact reaches through your call graph — so you can focus attention where it actually matters.**\n\nSpend code review effort where business risk is highest — not spread evenly across every diff.\n\n**Try LiveReview on your codebase:**", "url": "https://wpnews.pro/news/how-does-ai-know-what-s-inside-an-image-meet-vision-language-models", "canonical_source": "https://dev.to/rijultp/how-does-ai-know-whats-inside-an-image-meet-vision-language-models-2m8o", "published_at": "2026-09-02 19:29:03+00:00", "updated_at": "2026-09-02 19:53:48.778091+00:00", "lang": "en", "topics": ["computer-vision", "large-language-models", "artificial-intelligence"], "entities": ["Rijul", "LiveReview"], "alternates": {"html": "https://wpnews.pro/news/how-does-ai-know-what-s-inside-an-image-meet-vision-language-models", "markdown": "https://wpnews.pro/news/how-does-ai-know-what-s-inside-an-image-meet-vision-language-models.md", "text": "https://wpnews.pro/news/how-does-ai-know-what-s-inside-an-image-meet-vision-language-models.txt", "jsonld": "https://wpnews.pro/news/how-does-ai-know-what-s-inside-an-image-meet-vision-language-models.jsonld"}}