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.
How a Deep Think Verification Step Tripled My API Bill, and How thinking_level Got It Back
After wiring API-accessible Gemini 3 Deep Think into my output-verification step, my projected monthly cost jumped roughly 3x. Here is the implementation record of capping it with thinking_level and a cost guardrail, then settling on a two-stage design with Flash.
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.
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.
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.
Reading a Night of Logs in Three Minutes — Building My Own Daily Brief for Ops With the Gemini API
Inspired by Gemini's Daily Brief, I built a pipeline that turns overnight operations logs into one morning email: collect, summarize with response_schema, render, deliver — with measured token counts and a fallback that kept working through the June outage.
Is Anyone Actually Using Your Gemini Feature? Measuring Acceptance, Regeneration, and Edit Distance
Token charts will not tell you whether users embrace a Gemini-powered feature. A practical design for measuring acceptance rate, regeneration rate, and edit distance with Swift and BigQuery, with two weeks of real numbers.