Put Help Docs and Screenshots in One File Search Store and Return Answers That Cite the Image Too
Your text help docs and your screenshots live in separate stores, so a single question can never return both the steps and the matching screen. With gemini-embedding-2 going multimodal in File Search, here is how I merged them and returned the cited screenshot alongside the answer.
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.
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.
Rebuilding a Three-Layer RAG Cache After Migrating to Gemini 3.5 Flash
When Gemini 2.0 Flash was retired, I rebuilt my RAG caching stack around 3.5 Flash. Here are the working implementations for response, semantic, and embedding caches, measured hit rates from production, and how self-managed caching divides the work with the API's Context Caching.
Trimming Gemini Embeddings from 3072 to 768 Dimensions: A Matryoshka Approach to Cutting Vector DB Cost and Latency
gemini-embedding-001 returns 3072-dimensional vectors, but thanks to Matryoshka representation you can keep only the leading dimensions with almost no quality loss. This is a design for trimming to 768 to cut vector DB storage and latency, including the re-normalization pitfall and coarse-to-fine search code.
The Day You Switch Gemini Embedding Models: Designing a Zero-Downtime Reindex
Upgrade your embedding model and every vector you ever stored becomes incompatible. Here is a dual-index design for re-embedding hundreds of thousands of vectors without downtime, complete with a resumable reindex job and a query-side abstraction layer.
Building a RAG Evaluation Framework with Gemini API: RAGAS, LLM-as-Judge, and Custom Metrics Production Masterclass
Complete guide to building a quantitative RAG evaluation framework using RAGAS, LLM-as-Judge with Gemini API, and custom domain metrics — including CI/CD integration and production monitoring.
Choosing the Right Gemini RAG Pattern in 2026 — Simple vs Advanced vs Agentic, Compared with Real Code
Compare three RAG implementation patterns with the Gemini API — Simple, Advanced, and Agentic — using real code examples. Learn which pattern fits your use case and where to start.
Building a Fully Edge RAG with Gemini API and Cloudflare Vectorize: A Production Guide for Low Latency, Low Cost, Global Delivery
Combine Gemini Embedding with Cloudflare Vectorize to ship a production RAG that runs entirely inside the Workers runtime — global latency, predictable cost, and a defensive layer covering subrequest limits, retries, and tenant isolation.
Building GraphRAG with the Gemini API — A Complete Production Guide to Hybrid Knowledge Graph + Vector Retrieval
When pure vector search hits a wall on multi-hop, relational, and aggregation queries, GraphRAG fills the gap. This guide walks through a production hybrid GraphRAG architecture powered by Gemini 2.5 Pro and Flash, with working code.
Building a Production RAG System with Gemma 4: Local LLM + Vector Search Architecture
A complete guide to building production RAG systems with Gemma 4, ChromaDB, and pgvector. Covers architecture design, chunking strategies, Long-Context RAG using the 256K window, hybrid search, and performance optimization.
Gemini API Embeddings vs Vector Databases: Pinecone, Qdrant, pgvector, and Cloud Spanner Compared for Production
Benchmark Pinecone, Qdrant, pgvector, and Cloud Spanner Vector using Gemini text-embedding-004 with real latency, cost, and code. The definitive production selection guide.