Building a Production RAG Pipeline on Vertex AI

Why RAG Over Fine-Tuning?

Fine-tuning a model with your company's knowledge is expensive, slow to update, and opaque. RAG instead retrieves relevant context from a vector database at inference time and passes it to the LLM prompt. Your knowledge base updates in minutes, not weeks, and you can inspect every retrieved chunk.

The Architecture

A production RAG pipeline on GCP consists of:

1. Document ingestion: Cloud Storage → Cloud Run (chunking & embedding) → Vertex AI Vector Search
2. Query pipeline: API request → Cloud Run → embed query → Vector Search nearest-neighbour lookup → Gemini Pro with retrieved context → response
3. Observability: Cloud Logging, Cloud Trace, and custom latency metrics in Cloud Monitoring

Step 1 — Chunking Strategy

Chunk documents into ~512 token segments with a 64 token overlap. Smaller chunks increase retrieval precision; larger chunks give more context per retrieved doc. For structured documents (policies, technical specs), semantic chunking by section is often better than fixed-size chunking.

Step 2 — Embeddings with Vertex AI

Use the textembedding-gecko model family for text embeddings. The text-embedding-004 model (768 dimensions) offers an excellent balance of quality and cost. Batch-embed your corpus during ingestion for cost efficiency.

Step 3 — Vector Search Index

Vertex AI Vector Search (formerly Matching Engine) is fully managed and scales to billions of vectors. Create a streaming index for real-time updates or a batch index for offline-first workloads. Set the distance measure to DOT_PRODUCT_DISTANCE with normalised embeddings.

Step 4 — The Retrieval Prompt

A robust RAG prompt template:

You are a helpful assistant. Answer the question using ONLY the context provided below.
If the context doesn't contain enough information, say so.

Context:
{retrieved_chunks}

Question: {user_query}
Answer:

Step 5 — Evaluation

Use Vertex AI Rapid Eval with RAGAS metrics: faithfulness, answer relevancy, and context recall. Set automated regression gates in your CI/CD pipeline so a bad embedding model or prompt change doesn't reach production undetected.

Building a RAG pipeline? Let's talk — I can help you avoid the common pitfalls.