Grounding

Connecting model responses to verifiable sources (Google Search, Vertex AI Search, or your own data) to reduce hallucination and add citations. · Official docs

Related concepts

• Getting Started · Gemini Capabilities · Prompt Engineering · Function Calling & Tools · Agents & ADK · Agent Engine · RAG & Grounding · Vertex AI Search · Embeddings & Vector Search · Audio & Speech · Multimodal Live API · Image & Video Generation · Evaluation · Open & Partner Models · Responsible AI · MLOps & Deployment · Applied Use Cases

Used in 53 notebooks

Agents & ADK (1)

• Get started with Memory Bank on ADK — Build ADK agents that generate, retrieve, preload, and customize Agent Engine Memory Bank memories.

Embeddings & Vector Search (2)

• Getting Started with Text Embeddings + Agent Platform Vector Search — Builds text embeddings from Stack Overflow titles and serves semantic search with Vector Search.
• Using “task type” embeddings for improving RAG search quality — Shows how task-type embeddings improve RAG Q&A retrieval quality and MRR.

Gemini (41)

• Building a Conversational Search Agent with Agent Engine and RAG on Vertex AI Search — Builds and deploys a Gemini movie-search RAG agent with LangChain, Agent Engine, and Vertex AI Search.
• Function Calling Agent — Builds a Gemini function-calling retail assistant over Cymbal Retail data in BigQuery.
• Task Planner Agent — Builds a LangGraph task-planner agent that plans, searches with Gemini, reflects, and responds.
• Introduction to Gemini Deep Research Agent — Shows how to run Gemini Deep Research Agent with streaming, multimodal input/output, and grounding tools.
• Evaluate groundedness with custom parsing — Evaluates Gemini response groundedness with Vertex AI EvalTask and custom JSON parsing.
• Evaluate generated answers from Retrieval-Augmented Generation (RAG) using Rapid Evaluation and Dataflow ML with Vertex AI pipelines — Builds a Vertex AI Pipeline to batch-evaluate RAG Q&A outputs with Rapid Eval API and Dataflow ML.
• Evaluate Generated Answers from Retrieval-Augmented Generation (RAG) for Question Answering with Gen AI Evaluation Service SDK — Evaluates BYO RAG QA answers with Vertex AI Gen AI Evaluation, custom metrics, and result visualizations.
• Intro to Gemini 2.5 Flash — Introduces Gemini 2.5 Flash on Vertex AI with text, thinking, multimodal, tools, and structured output.
• Intro to Gemini 2.5 Flash-Lite — Introduces Gemini 2.5 Flash-Lite on Vertex AI with prompting, thinking, structured output, search, and code tools.
• Intro to Gemini 2.5 Pro — Introduces Gemini 2.5 Pro on Vertex AI using the Google Gen AI SDK for text, multimodal, tools, and reasoning.
• Gemini 3.1 Flash Image (Nano Banana 2 🍌) Generation — Shows Gemini 3.1 Flash Image generation, grounding, video input, and image editing with Google Gen AI SDK.
• Intro to Gemini 3.1 Flash-Lite — Quickstart for Gemini 3.1 Flash-Lite with Google Gen AI SDK on Google Cloud.
• Intro to Gemini 3.1 Pro — Quickstart for Gemini 3.1 Pro on Vertex AI with Google Gen AI SDK API features.
• Intro to Gemini 3.5 Flash — Quickstart for Gemini 3.5 Flash generation, multimodal inputs, tools, grounding, and safety with Google GenAI SDK.
• Intro to Gemini 3 Flash (Preview) — Quickstart for Gemini 3 Flash Preview on Vertex AI with Gen AI SDK features and multimodal/tool examples.
• Gemini 3 Pro Image (Nano Banana Pro 🍌) Generation — Shows how to generate and edit images with Gemini 3 Pro Image using the Google GenAI SDK.
• REST API — Uses cURL to call Gemini 3.5 Flash on Vertex AI for text, chat, tools, multimodal, and JSON output.
• Getting started with Gemini using Vertex AI in Express Mode — Introduces Gemini on Vertex AI Express Mode using the Google Gen AI SDK.
• Grounding with Vertex AI Search — Creates a Vertex AI Search engine and uses it to ground a Gemini response with retrieved context.
• Intro to Grounding with Gemini in Vertex AI — Shows how to ground Gemini 3.5 Flash responses with Search, Enterprise Web Search, Maps, and Vertex AI Search.
• Intro to Logprobs — Introduces Gemini logprobs on Vertex AI for confidence, autocomplete, and RAG grounding analysis.
• Getting Started with Gemini Live API using Gen AI SDK — Introduces Gemini Live API audio sessions, tools, transcription, and VAD with the Google Gen AI SDK.
• Interactive Loan Application Assistant (Financial Services) — Builds a Gemini 2.0 loan document assistant with RAG, large context, audio, Vertex AI Search, and Vector Search.
• Real-time Retrieval Augmented Generation (RAG) using the Multimodal Live API with Gemini 2.0 — Builds a retail RAG pipeline with Gemini Multimodal Live API for grounded text and audio answers.
• LlamaIndex RAG Workflows using Gemini and Firestore — Builds a LlamaIndex RAG workflow with Gemini, Vertex embeddings, and Firestore storage.
• Intro to Building a Scalable and Modular RAG System with RAG Engine in Vertex AI — Builds a Vertex AI RAG Engine corpus, imports files, retrieves context, and grounds Gemini or Llama responses.
• Vertex AI Rag: Cross-Corpus Retrieval with AskContexts and AsyncRetrieveContexts Demo — Demonstrates cross-corpus Vertex AI RAG retrieval with ask_contexts and async_retrieve_contexts.
• Evaluating Vertex RAG Engine Generation with Vertex AI Python SDK for Gen AI Evaluation Service — Evaluates Vertex AI RAG Engine responses with a custom Gen AI Evaluation Service metric.
• Vertex AI RAG Engine with Vertex AI Feature Store — Builds a Vertex AI RAG Engine corpus backed by Vertex AI Feature Store and queries it with Gemini.
• Vertex AI RAG Engine with Pinecone — Shows how to use Vertex AI RAG Engine with Pinecone as the vector database for Gemini retrieval.
• Gen AI & LLM Security for developers — Shows prompt injection attacks against Gemini and layered mitigations with DLP, NL API, safety filters, and embeddings.
• Intro to Url Context — Shows how to use Gemini URL context to summarize, compare, and analyze web pages with optional Google Search grounding.
• AI-Assisted Data Science Workflows in BigQuery — Builds a BigQuery multimodal housing workflow with Gemini enrichment, BQML clustering, and vector search.
• Use Retrieval Augmented Generation (RAG) with Gemini API — Builds a LangChain RAG pipeline over GitHub code notebooks using Gemini and Vertex AI embeddings.
• Get hands-on with a customer support use case using Gemini and Gen AI SDK — Builds a Gemini customer-support flow for retail product matching, room-fit reasoning, tools, search, and Live API audio.
• GraphRAG on Google Cloud With Spanner and Vertex AI Agent Engine — Builds a GraphRAG Q&A agent with Spanner Graph, Gemini, ADK, and Vertex AI Agent Engine.
• 🌿 Eco-Nomad Swarm: 100% Real-Data Sustainable Travel Orchestration — Builds a Vertex AI ADK travel swarm using Neo4j GraphRAG and live APIs for sustainable trip briefs.
• Know Your Customer Use Case - Gemini Grounding with Google Search — Builds grounded Gemini KYC negative-news reports and evaluates response quality with custom Vertex AI metrics.
• Retail AI Location Strategy: Autonomous Site Selection & Market Analysis — Builds a Gemini 3 retail site-selection pipeline using search, Maps, code execution, reasoning, and JSON output.
• Leverage LlamaIndex with Vertex AI Vector Search to perform question answering RAG — Builds LlamaIndex RAG on Vertex AI Vector Search, compares prompts, and adds multi-document agents.
• Retrieval Augmented Generation(RAG) with AlloyDB — Builds a RAG workflow over patent abstracts using AlloyDB vector search and Gemini on Vertex AI.

Open Models (1)

• Cloud Run GPU Inference: Gemma 2 RAG Q&A with Ollama and LangChain — Deploys Gemma 2 on Cloud Run GPU with Ollama and builds a LangChain RAG Q&A chain.

Vertex AI Search (8)

• Create a Vertex AI Datastore and Search Engine — Creates a Vertex AI Search datastore, imports GCS PDFs, creates an enterprise search engine, and queries it.
• Custom Embeddings with Vertex AI Search — Builds a Vertex AI Search app using custom text-embedding-005 embeddings from Stack Overflow data.
• Gemini Enterprise answer eval using BLEU, ROUGE, BERT, Similarity Score — Evaluates Gemini Enterprise answers against a golden dataset with NLP metrics and saves results.
• Defining custom attributes based on URL patterns in Vertex AI Search Website Datastores — Shows how to add URL-pattern custom attributes to a Vertex AI Search website datastore.
• Inline Ingestion of Documents into Vertex AI Search — Shows inline rawBytes document ingestion, document operations, search, filters, and cleanup in Vertex AI Search.
• Parsing and Chunking in Vertex AI Search: Featuring BYO Capabilities — Retrieves, reviews, exports, and reimports Vertex AI Search parsed and chunked documents with BYOC.
• Query-Level Boosting, Filtering, and Facets for Vertex AI Search Website Datastores — Configures Vertex AI Search website datastore schema, filters, facets, and boosting via REST.
• Building Search Applications with Vertex AI Search — Builds Vertex AI Search workflows using Search API, Gemini grounding, and LangChain retrieval.

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