# How Gemini AI Works: Multimodal Models, Tools & Images > Learn how Gemini AI works across multimodal inputs, context, grounding, tool use, image generation, and editing - and what Google does not disclose. - Author: Swarnava Dutta (https://swarnava.dev) - Published: 2026-08-08 - Tags: Gemini Google AI, Gemini Multimodal Model, Gemini AI Architecture - Reading time: 11 min (2520 words) - Canonical: https://swarnava.dev/blogs/how-gemini-ai-works --- ![Illustration of how gemini ai works: A wide conveyor belt feeding mixed items—photo, sound wave card, text page—into a funnel](/images/blogs/how-gemini-ai-works-hero.jpg) I spent a Saturday afternoon fighting with a "simple" feature request: let users upload a photo, ask Gemini to describe it, then edit a detail in the same conversation. Half the responses ignored the image entirely, and I burned an embarrassing amount of time assuming I'd broken the API call before realizing I'd misunderstood how the model actually processes mixed inputs. That debugging session is basically why this article exists - most people asking how Gemini AI works picture a single chatbot brain, when it's actually a pipeline of cooperating systems stitched together behind a friendly chat window. Gemini isn't one model wearing a chat interface. It's a product layer sitting on top of a multimodal model family, a retrieval and grounding system, a tool-calling layer, and separate image generation and editing pipelines that all hand work back and forth. Understanding where each piece starts and stops is the difference between building something that reliably handles a photo-plus-text prompt and something that silently drops half your input. This piece traces that whole path - tokenization to grounding to tool use to image generation - while being explicit about what Google actually documents versus what's reasonable architectural inference. ## How Gemini AI Works From Multimodal Prompt to Response When you type or drop a photo into the Gemini app, that request touches a chain of systems before anything comes back. The product layer preprocesses your input, decides which model variant handles it, assembles context, runs inference, optionally pulls in grounding or tool results, screens for safety, then delivers the response. None of that is a single "brain" - it's the Gemini app, the underlying model family, developer APIs, Google Search, and separate image-generation systems working as related but distinct components. The exact route your prompt takes isn't fixed. It shifts based on the prompt itself, which features are enabled, your account tier, the model version deployed that week, and whether you're in the consumer app or calling an API directly. A free-tier photo-editing request and a paid API call with function calling enabled can traverse genuinely different paths even with identical text. ### How Text, Images, Audio, and Video Become Model Inputs Text gets segmented into tokens - the standard approach across large language models generally [[1]](#ref-1). Images, audio, and video get converted into machine-readable representations the model can process alongside text, though Google doesn't publish exact encoder details. All of that - your instructions, uploaded files, conversation history, retrieved documents, and tool outputs - competes for space in one finite context window. That's precisely where my Saturday debugging session went wrong: I'd stuffed enough conversation history and image data into one turn that the model deprioritized the actual image reference. Treat any frame-sampling or encoding specifics you read online as plausible patterns, not confirmed Gemini internals. ### Context Processing, Model Routing, and Output Generation Once inputs are encoded, attention-based processing lets the model weigh which tokens matter most for generating the next one - a probabilistic process, not exposed step-by-step reasoning. Products built on Gemini often route a request to a lighter or heavier model variant depending on latency, cost, and modality needs, similar to routing patterns seen in [large language models generally](/blogs/how-large-language-models-work). Output isn't always plain text either - it can be structured JSON, a tool call request, a citation-backed answer, or generated media, depending on what the request actually needs. ## Gemini Multimodal Model Architecture and the VLM Concept A vision-language model (VLM) is trained so visual information and natural-language instructions share the same representational space, letting one model both "see" an image and reason about it in text. That's a real architectural distinction, not marketing language - older pipelines bolted a separate vision system onto a language model, having it generate a caption first, then feeding that caption as plain text into the LLM. Anything visual not captured in the caption - layout, spatial relationships, subtle detail - simply vanished before the LLM ever saw it. Gemini's multimodal training instead lets the model handle images, audio, video, and text within one architecture, which is why it can do image understanding, document analysis, chart interpretation, visual question answering, audio comprehension, and video reasoning without a captioning bottleneck in between. ![Comparison of an older vision-caption-LLM pipeline and a multimodal VLM connecting image input directly to a natural-language response.](/images/blogs/how-gemini-ai-works-diagram-1.jpg "Older vision pipeline vs multimodal model") One caution worth repeating: Gemini is a shifting family of models, not one static artifact. Architecture claims should always be tied to a specific model version and date, because what's true of one release may not hold for the next. ### What Google Documents - and What Architecture Diagrams Infer Draw a hard line between four tiers: confirmed product behavior, Google's own technical statements, reasonable industry inference, and unsupported speculation. Transformer-based processing and multimodal training are documented at a high level [[2]](#ref-2), but encoder details, routing logic, parameter counts, training mixtures, and internal safety stacks are often undisclosed or vary by model. Treat any simplified architecture diagram you find online as a teaching aid, not a literal map of production infrastructure. ## Gemini Grounding and RAG: How External Knowledge Enters Retrieval-augmented generation means fetching external information and handing it to the model as context before it writes an answer, instead of relying purely on what it learned during training. Gemini can pull that context from several distinct sources, and conflating them causes most of the "why did it cite that" confusion I see from developers. - Google Search grounding - live web results injected at query time. - Private corpus retrieval - your own documents, indexed and searched. - Long-context prompting - pasting entire documents directly into the prompt, no retrieval step at all. - Trained-in knowledge - whatever the model memorized, ungrounded and unattributed. - Live tool calls - an API hit at inference time, covered next. Grounding improves freshness and gives you a traceable citation trail, but it doesn't guarantee every sentence in the output is actually supported by what got retrieved. A model can retrieve five accurate documents and still synthesize a claim none of them make. Citations shown next to an answer only mean sources were retrieved - not that every clause traces back to one. ### Example RAG Pipeline: Retrieve, Rank, Generate, and Cite Picture answering "what's the rate limit on this endpoint" from your internal API docs. The pipeline interprets the query, searches a chunked, indexed version of the docs, reranks candidates for relevance, assembles the top passages into context, then generates an answer with citations pointing at the winning chunks. Failure points cluster predictably: chunking that splits a table mid-row, a stale index missing last week's doc update, retrieval that surfaces adjacent-but-wrong endpoints, or a document containing injected instructions that hijack the generation step. I've seen synthesis go wrong even with perfect retrieval - the model blends two correct chunks into one incorrect claim. ## How to Evaluate a Gemini Grounding or RAG Pipeline Separate two questions that people constantly merge: did we retrieve the right sources, and did the model actually use them correctly. A fluent, confident answer can mask a retrieval step that pulled irrelevant chunks - the model just papers over the gap with something plausible. On the retrieval side, track: - Recall@k and precision@k - did the needed evidence show up, and how much noise came with it - Ranking quality - is the best evidence near the top, or buried at position eight - Evidence coverage - does the retrieved set actually contain everything needed to answer fully On the generation side, check answer correctness against ground truth, faithfulness to the retrieved context (not just factual truth in general), and whether cited sources actually entail the claims attached to them. Completeness, latency, cost per query, and whether the system abstains when it should all matter too - a model that guesses instead of saying "I don't know" is a liability in production. Build test sets deliberately, not from whatever queries happened to come in last week. Include fresh facts outside training data, ambiguous questions, cases with no valid answer, conflicting sources, adversarial documents designed to hijack instructions, and permission-restricted content that should never surface. For anything high-stakes or subjective, keep a human reviewing outputs - automated metrics don't catch a technically-cited answer that's still misleading. ## How Gemini AI Works With Tools and Function Calling Gemini function calling works as a proposal loop, not a remote-control loop. The model looks at your prompt, decides a function would help, and returns structured output naming the function and its arguments - it never touches your database or API directly. Your application validates those arguments, checks permissions, executes the actual call, and feeds the result back to the model for a final natural-language response. That separation matters: the model suggests, your code decides whether the suggestion is safe to run. This is the same pattern underpinning most [agent workflow](/blogs/how-llm-guardrails-work) designs today. ![Control loop showing Gemini proposing a function call, the host application validating and executing it, and tool results returning for the final response.](/images/blogs/how-gemini-ai-works-diagram-2.jpg "How Gemini function calling is controlled") A tool schema defines the function name, argument types, and required fields, and Gemini fills that schema rather than inventing an ad-hoc call. Some workflows need parallel calls - checking weather and calendar simultaneously - others need strict sequencing where step two depends on step one's output. Handle malformed arguments and failed calls explicitly, and require human confirmation before anything destructive: deleting records, sending money, sending irreversible messages. Security risks compound quickly: over-permissioned tools, prompt injection hidden in retrieved documents instructing the model to call a tool it shouldn't, and untrusted tool output feeding straight back into generation. Treat every tool result as untrusted input, same as user text. ## Gemini Image Generation and Photo Editing Workflows Understanding a photo and generating one are different operations wearing the same chat window. Image understanding feeds pixels into the model as input tokens for reasoning; image generation runs a synthesis pipeline that produces new pixels as output. Gemini's interface orchestrates both, plus editing, without exposing exactly how those handoffs happen internally. What the model is capable of and what you can actually access aren't the same thing. Availability shifts by region, account tier, which model variant you're on, and current safety policy - a feature working in one country's app can be absent elsewhere. Always check current documentation rather than trusting a screenshot from a few months back. ### From Prompt to Generated Image The pipeline interprets your prompt, sometimes refines it internally, synthesizes an image, runs safety checks, then delivers the result - often with provenance indicators like SynthID watermarking baked in [[3]](#ref-3). Composition, subject, style, lighting, and aspect ratio all steer the output, and requests involving rendered text are still one of the harder cases for any generator. Expect occasional failures too: garbled text, warped hands, inconsistent identity across a series, or spatial relationships that don't quite hold together. ### How Gemini's Photo Editor Applies Targeted Changes Editing conditions the model on both your source image and an instruction - "remove the sign," "swap the background," "restyle as watercolor." Whether Gemini exposes explicit masks or region selection internally isn't publicly detailed, so treat that as a general editing concept, not a confirmed implementation. Always compare output against the original: unintended changes, identity drift, and altered fine details creep in more often than a quick glance catches. ## Limits, Privacy, and Claims Gemini Users Should Verify Every modality has its own failure mode, and they compound in a mixed conversation. Text generation hallucinates confident facts, vision misreads charts or spatial layout, grounding can cite a source that doesn't actually entail the claim next to it, tool calls arrive with malformed or wrong arguments, and image edits drift from the source in ways that don't survive a screenshot glance. Treat all five as expected behavior, not edge cases. Privacy and retention aren't one universal answer either. What happens to an uploaded photo, whether a connected service can read your calendar, how long conversation history persists, and what enterprise protections apply all depend on the specific Gemini product and account setup - consumer app, API, or Workspace tier. A Workspace admin's data controls are not your personal app's defaults. Don't generalize from one interface to the whole product family. Check the current model card, API docs, and Workspace or Cloud terms before making a claim about what "Gemini" does - features and availability shift release to release. Before shipping anything consequential, run a quick pass: - Inspect grounding sources, don't trust citations blindly - Review tool call parameters before execution - Keep sensitive data out of prompts you don't control - Compare edited images against the original - Require human approval on irreversible actions ## FAQ ### What Are Vision Language Models (VLMs)? A vision-language model trains images and text into a shared representational space, so one network can both interpret visual content and reason about it in natural language. That's different from bolting a captioning step onto a text-only model, where anything the caption missed - spatial layout, subtle detail - never reaches the language model at all. Gemini's multimodal design follows this unified approach rather than the older caption-then-generate pattern. ### What is Retrieval Augmented Generation (RAG)? RAG fetches relevant external documents at query time and hands them to the model as context before it writes an answer. Instead of relying solely on memorized training data, the model grounds its response in retrieved passages, which improves freshness and gives you a traceable citation path. It doesn't guarantee every sentence is fully supported by what was retrieved, though - synthesis errors still happen. ### Can you provide an example of a RAG pipeline? Yes - imagine querying internal API docs for a rate limit. The system interprets the query, searches a chunked index, reranks candidates, assembles top passages into context, then generates a cited answer. Common failure points include chunking that splits a table mid-row or a stale index missing recent updates. ### How do I evaluate my RAG pipeline? Separate retrieval quality from generation quality: track recall and precision at k, ranking position of the best evidence, then check answer correctness, faithfulness to retrieved context, and citation entailment. Build adversarial and edge-case test sets deliberately, and keep human review on anything high-stakes. ### how gemini ai photo editor Gemini's photo editor conditions the model on your source image plus a text instruction - like "remove the sign" - and outputs a modified image through the same synthesis pipeline used for generation. Whether it uses explicit masks internally isn't publicly documented. Always compare the edited result against the original for identity drift or unintended changes. ## References 1. [Improving Self Consistency in LLMs through Probabilistic Tokenization](https://arxiv.org/abs/2407.03678v1) - Ashutosh Sathe, Divyanshu Aggarwal, Sunayana Sitaram (2024) 2. [Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models](https://arxiv.org/abs/2312.17661v1) - Yuqing Wang, Yun Zhao (2023) 3. [SynthID-Image: Image watermarking at internet scale](https://arxiv.org/abs/2510.09263v1) - Sven Gowal, Rudy Bunel, Florian Stimberg et al. (2025)