Three Places Gemini API Embedding Broke on Me — and What Actually Fixed Them
Notes from wiring Gemini API Embedding into an auto-categorization pipeline: INVALID_ARGUMENT from dimension mismatches, 429 rate limits that lost 400 items of work, and weak retrieval quality — plus normalization, checkpointing, and a recall@5 harness that made improvements measurable.
Gemini API DEADLINE_EXCEEDED Errors: Five Things to Check First
When DEADLINE_EXCEEDED suddenly starts spiking on your Gemini API backend, here are the five checks I run first — based on real production debugging.
Why count_tokens Lies: 5 Reasons Your Gemini API Bill Is Higher Than You Estimated — A Reconciliation Playbook
count_tokens said 1,200 tokens. Cloud Console billed you for 4,800. I made the same mistake building my first indie app on Gemini. This guide walks through the five hidden contributors — thinking, tools, multimodal, history, caching — and how to reconcile them with reproducible code.
When Gemini API URL Context Returns Nothing: A Diagnostic Walkthrough
If Gemini's URL Context tool stays silent or returns generic answers, the cause is almost always one of three things: tool configuration, URL formatting, or site-side restrictions. Here's how to isolate which.
Why Your Gemini File URI Suddenly Returns 404 — Designing Around the 48-Hour TTL
Your Gemini-powered image or video pipeline worked perfectly yesterday, then started returning 404 the morning after a restart. The culprit is the File API's 48-hour TTL. Here is how to detect it and design an app that survives it.
Classify Gemini API Errors by Status — Handling 429, SAFETY, and Token Limits in Production
Sort Gemini API failures along two axes — HTTP status and finish_reason — so you know instantly whether to wait or to fix. Covers the three distinct 429 limits, model-deprecation fallbacks, and how the exception hierarchy changes after moving to the google-genai SDK.
When a Prompt That Worked in AI Studio Quietly Breaks Over the API — Field Notes on Measuring the Difference
A prompt that behaves perfectly in AI Studio returns an empty string or a 404 the moment you call the Gemini API from your own code. Instead of eyeballing the two, here is a small harness that records the config diff plus finish_reason, token usage, and the model name the server actually resolved — so you can isolate the cause by layer.
Stopping Gemini API Function Calling Loops: Why They Happen and How to Break Them
Your tool-calling agent keeps invoking the same function and never finishes. Here is how to diagnose the loop and bake stop conditions into your prompt, code, and tool responses — including catching regressions when the default model changes and detecting result-based stalls.
Gemini API on Google Cloud: Diagnosing Production Errors Layer by Layer
Systematically diagnose Gemini API errors in Google Cloud production environments. Covers IAM permissions, Vertex AI vs AI Studio, VPC Service Controls, quota management, service accounts, and multi-region failover with full code examples.
How to Handle Gemini API Model Deprecation and Migration Errors
A practical guide to migrating from deprecated Gemini API models and resolving common migration errors.
Gemini API Implicit Caching Not Working — Troubleshooting Guide by Root Cause
Troubleshoot Gemini API implicit caching issues: cache not hitting, unexpectedly high costs, or low cache hit rates. Covers token thresholds, prompt structure, model version consistency, TTL expiry, and multimodal caching with code examples.
When Gemini API Cuts Your Response Off Mid-Sentence — Detecting finish_reason: MAX_TOKENS and Stitching the Continuation
Long-form generation that ends mid-sentence is usually finish_reason: MAX_TOKENS. This failure arrives as a quiet HTTP 200, no exception. Here is how to detect it, stitch a continuation to recover the full text, and avoid the thinking-token trap that makes it worse on 3.x models.