All Articles
Gemini Function Calling in Production — Design, Implementation, and Debugging
A practical guide to making Gemini Function Calling work reliably in production. Covers function schema design, parallel calling, retry and timeout patterns, and debugging techniques for the issues that actually show up in real applications.
Managing Gemini API with LiteLLM — A Practical Guide to Running Multiple AI APIs Together
Learn how to use LiteLLM to manage Gemini API alongside Claude and OpenAI. This practical guide covers unified interfaces, fallback configuration, and cost tracking for multi-LLM setups.
Gemini API Returns Markdown — How to Get Plain Text Responses
Gemini API responses often contain Markdown symbols like **, ##, and -. Learn how to get clean plain text using response_mime_type, System Instructions, and post-processing with practical Python and TypeScript code examples.
NotebookLM Not Working? Fix Sources, Podcasts & Response Quality Issues
A practical guide to the most common NotebookLM problems: PDF upload failures, podcast generation errors, and off-target responses. Includes real fixes for each scenario.
Google Cloud Workflows × Gemini API Production Orchestration Guide: Timeouts, Retries, and Cost Control
A complete guide to orchestrating Gemini API calls in production using Google Cloud Workflows. Covers YAML step definitions, automatic retries, timeout configuration, and cost budget alerts with working code examples.
Building a Real-World Data Processing Agent with Gemini API: Integrating Function Calling, Code Execution, and Grounding
Learn how to combine Gemini API's three core tools—Function Calling, Code Execution, and Grounding—to build production-grade agents that access external APIs, run Python code, and retrieve real-time web data. Complete implementation guide with working code.
Controlling Gemini 2.5 Pro's Thinking — Thinking Budget and Reasoning-Aware Prompt Design
A deep dive into Gemini 2.5 Pro's Thinking feature and internal reasoning process. Covers Thinking Budget configuration, optimal values by task type, extracting thinking_parts for quality verification, and prompt design patterns that maximize reasoning quality.
Android Bench 2026: How to Read On-Device AI Performance Rankings
Android Bench is Google's benchmark for measuring AI inference performance on real Android devices. Here's what the numbers mean, how Gemma 4 fits in, and what on-device AI performance actually matters for users and developers.
Gemini API × Gemma 4 Hybrid Inference Architecture: A Complete Production Guide to Cutting API Costs by 70%
Learn how to build a hybrid inference architecture combining Gemini API and Gemma 4 local models. Covers request routing design, cost analysis, and production deployment — with complete Python code.
Gemini Computer Use Tested: What It Can Actually Do, Where It Breaks, and Whether It's Production-Ready
Three real-world scenarios tested with Gemini's Computer Use capability: web data collection, PDF extraction with email drafting, and cross-window data reconciliation. Honest results on accuracy, speed, and cost.
Gemini Code Assist Outline Feature: Generating Code Architecture Before Writing a Single Line
Gemini Code Assist's outline feature lets you generate the skeleton of functions, classes, and modules before writing implementation. Here's how it works, when it helps most, and how to configure it for your project.
Google Sheets API × Gemini API: A Python Data Pipeline — No Apps Script Required
Learn how to build a fully Python-based pipeline that reads data from Google Sheets, processes it with Gemini API, and writes results back — without touching Apps Script. Covers service account auth, structured output, and rate limit handling.