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Building with Gemini

Google's Multimodal Powerhouse

Google's multimodal powerhouse — master Gemini 2.0 Flash, Pro, and Ultra, harness the 1M token context window, process images/audio/video natively, and build production pipelines with the Gemini API.

8 lessons~78 min total

Lessons are shown in recommended order. Complete them in sequence for the best experience — or jump to any lesson.

Lesson 88·9 min read

Why Gemini? The Multimodal Advantage

Gemini is not another chatbot. It is a native multimodal processing core with a 1M token context window and deep Google ecosystem integration — and understanding why that matters changes how you build AI pipelines.

Lesson 89·9 min read

Google AI Studio and the Gemini API

AI Studio is where you explore. The Gemini API is where you build. Vertex AI is where you scale. Understanding which tool belongs at which stage eliminates 80% of the setup friction operators hit when starting with Gemini.

Lesson 90·10 min read

Gemini Model Routing — Flash, Pro, Ultra

Routing model selection is not a technical detail — it is an economics and quality decision. Flash, Pro, Ultra, and the experimental Thinking mode each occupy a distinct niche. Know the niche, route correctly, control costs.

Lesson 91·10 min read

Multimodal Inputs — Images, Audio, Video, and Documents

Gemini's native multimodality is its most distinctive capability. Send images, audio, video, and PDFs through one API. This lesson covers exactly how each input type works, the code patterns, and the real-world use cases that justify the architecture.

Lesson 92·10 min read

Long Context Mastery — The 1M Token Window

1M tokens is not just a bigger context window — it is a different way of working with large information sets. No chunking, no RAG pipelines for simple retrieval, no stitching. Load it all and ask. This lesson shows you when that changes everything.

Lesson 93·9 min read

Imagen — Google's Visual Intelligence

Imagen 3 is Google's production image generation model — photorealistic output, fine-grained style control, and an API pattern that integrates cleanly into automated pipelines. This lesson covers the mechanics, the prompting differences, and when to route to Imagen versus other providers.

Lesson 94·10 min read

Gemini in Production — MCP Servers and API Integration

Getting Gemini working in a notebook is different from running it reliably in production. Rate limits, retry logic, streaming, cost tracking, and the MCP server pattern — this lesson covers the operational layer that makes Gemini pipelines stable at scale.

Lesson 95·11 min read

Building a Gemini-Powered Pipeline

Theory becomes real when you build the full pipeline: multimodal input, Gemini processing, structured output, downstream action. This lesson walks through a production document extraction pipeline — every step, every edge case, every fallback.