Don't Break When the Default Model Moves: A Startup Capability-Probing Layer for Gemini
Pinning a model name breaks on deprecation; trusting the default breaks when the weights swap silently. This is the design I settled on: probe what the served model can actually do at startup, then build every request from that answer. Includes runnable Python.
When Your Firestore × Gemini Embeddings RAG Quietly Degrades — Designing for Re-Embedding
A RAG built on Firestore native vector search and Gemini Embeddings drifts when the embedding model changes generations, and retrieval quality drops with no errors. Here is how to detect the drift, re-embed without downtime, and keep retrieval cost in check.
When the Default Model Silently Upgrades: Catching Prompt Regressions in Numbers
Gemini 3.5 Flash is now the default and you can no longer turn it off. Assuming your responses can shift without you touching the prompt, here is how to bundle prompt, model, and sampling into one variant and catch regressions with canaries and an LLM judge — in working code.
Defending Against Prompt Injection When You Pass External Text to the Gemini API
User reviews, scraped articles, and other untrusted text are the entry point for indirect prompt injection when you feed them to the Gemini API. Here is a prioritized, code-backed defense you can drop into a production pipeline: trust-boundary isolation, schema constraints, a two-stage screening pass, and output sanitization.
Permission-Aware RAG — Designing Gemini Search That Only Cites What the User Is Allowed to See
The day you add RAG to internal search, drafts and finance memos nobody should see start leaking into answers. This is a production design — metadata filtering, defense in depth, and audit logging — for letting Gemini search while respecting permissions, with working code.
Trusting Gemini Structured Output in Production — Schema Design, Double Validation, and Bounded Retries
Gemini's structured output guarantees parseable JSON, not correct values. Notes on schema design with @google/genai, why propertyOrdering matters, a Zod double-validation layer, handling MAX_TOKENS truncation, and a bounded-retry extraction pipeline.
Keeping Gemini API's Default-Model Shift From Becoming an Incident — Pinning Model IDs and Detecting Silent Upgrades in Production
When the default model quietly moves up, your output length, reasoning behavior, and cost change with zero code edits. This guide shows how to pin model IDs in a single source of truth and verify the effective model from the response to detect default changes.
Controlling Image Tokens with the Gemini API media_resolution Setting — Tuning Batch Image Classification by Measurement
media_resolution, introduced in the Gemini 3 line, switches how many tokens an image input consumes across three levels. Through real batch-classification measurements, this guide shows how to balance cost and accuracy by assigning the right tier per task.
Gemini's GA Image Models Won't Output Exact Device Resolutions — A Wallpaper Pipeline That Fixes Aspect Ratio and Safe Areas
After switching to the GA image models, your wallpapers no longer fit the screen. Here's how to crop one master image into every device resolution and cut your generation count to a fraction, with full Pillow code.
Where to Adopt Gemini 3.5 Flash GA First — Per-Workload Evaluation and a Staged Rollout with a Model Router
How I migrated production workloads to Gemini 3.5 Flash GA in stages: a per-workload evaluation harness, measured results, an env-based model router, and rollback design.
The Morning Gemini Generated Fine but the Publish Crashed — A 'Generation Outbox' So Expensive Output Is Never Lost
Generation succeeds, then the process dies right before publishing. The expensive output is gone, and you pay for the same generation again. Here is a 'generation outbox' that persists the output first and turns publishing into an idempotent follow-up, plus what it did for me during the June outage.
Reconciling Orphaned Gemini Files API Uploads Across a Fleet of Apps
Files API uploads quietly expire after 48 hours. Here's how I keep orphaned files and quota under control across six apps, using reconciliation against my own database and a scheduled cleanup job — written up as production notes from running wallpaper apps.