Techniques to improve gen AI model output · Topic 4 of 5
Grounding and RAG
Grounding LLMs in first-party, third-party and world data, how RAG changes output, and Google Cloud's grounding offerings, including Google Search.
- 4 min
- Grounding ties output to verifiable sources.
- Sources: first-party enterprise data, third-party data, world data.
- RAG retrieves relevant content and adds it to the prompt; it improves accuracy, freshness and verifiability.
What it is
Grounding connects a model's output to verifiable sources of information, so answers are based on facts rather than only on what the model learned in training. The exam distinguishes grounding with first-party enterprise data, third-party data and world data, asks how retrieval-augmented generation (RAG) affects output, and lists Google Cloud's grounding offerings: prebuilt RAG with Agent Search, RAG APIs and grounding with Google Search.
How it works
Three kinds of grounding data
| Grounding source | What it is | Example | Google Cloud option |
|---|---|---|---|
| First-party enterprise data | The organization's own data: documents, databases, policies, product catalogs | HR policies, internal knowledge base, order records | Prebuilt RAG with Agent Search, RAG APIs |
| Third-party data | Data licensed or obtained from outside providers | Market data, industry reference datasets, partner catalogs | Ingest into Agent Search or a RAG pipeline |
| World data | Public, up-to-date information about the world | News, public facts, recent events | Grounding with Google Search |
"Our own policies" means first-party grounding. "What happened in the news this week" means world data through Google Search.
How RAG changes the output
Without RAG, a model answers from its training alone. With RAG:
| Effect | Why |
|---|---|
| More accurate, fewer hallucinations | The model writes from retrieved facts instead of guessing |
| Up to date | Retrieved content can be current, beyond the knowledge cutoff |
| Specific to the organization | Answers reflect private data the model never saw in training |
| Verifiable | Answers can cite the sources used, so users can check them |
| Quality depends on retrieval | If retrieval returns the wrong passages, or the sources are outdated, the answer suffers |
The last point matters: RAG is only as good as the data and search behind it. Keeping sources current and well organized is part of the job.
Google Cloud grounding offerings
| Offering | What it is | Choose it when |
|---|---|---|
| Pre-built RAG with Agent Search | A managed engine that ingests your data, indexes it, retrieves relevant content and generates grounded answers | You want grounded answers over your content quickly, with little engineering |
| RAG APIs | APIs for building your own RAG pipeline step by step (document parsing, embeddings, ranking, grounding checks) | You need control and customization of each step |
| Grounding with Google Search | Grounds Gemini's responses in fresh results from Google Search | Answers need current public information |
Grounding compared with other techniques
| Need | Technique |
|---|---|
| Facts that change often or are private | Grounding / RAG |
| A consistent format or tone | Prompt engineering |
| A deeply specialized behavior | Fine-tuning |
| Verification of high-impact outputs | Human in the loop |
A worked scenario
A pharmaceutical company builds an assistant for its sales team. Product labels and internal guidance are first-party data, grounded through prebuilt RAG with Agent Search so every answer cites the approved document. A licensed drug interaction database is third-party data added to the same search. Questions about recent public health news use grounding with Google Search. When the company updates a label, the assistant's answers change immediately, with no retraining.
How the exam asks about this
- A grounding source is described and you classify it as first-party, third-party or world data.
- "The assistant must answer about current public events." Grounding with Google Search.
- "Grounded answers over our documents with minimal engineering." Prebuilt RAG with Agent Search.
- "What happens to RAG answers if source documents are outdated?" The answers are outdated too.
What to remember for the exam
- Grounding ties output to verifiable sources.
- Sources: first-party enterprise data, third-party data, world data.
- RAG retrieves relevant content and adds it to the prompt; it improves accuracy, freshness and verifiability.
- RAG quality depends on the quality and freshness of the sources and the retrieval.
- Google Cloud offerings: prebuilt RAG with Agent Search, RAG APIs, grounding with Google Search.
Grounding with Google Search uses public web information. It does not search your private company documents.
Common mistakes
Retraining to add new documents. RAG uses new documents immediately.
Assuming RAG guarantees correct answers. Wrong or stale sources produce wrong answers.
Using Google Search grounding for internal policies. Use enterprise grounding for first-party data.
Check yourself
What is grounding?
Connecting a model's output to verifiable sources of information so answers are based on facts.
Which Google Cloud offering grounds answers in current public web information?
Grounding with Google Search.
Name two effects of RAG on generated output.
Any two of fewer hallucinations, up-to-date answers, organization-specific answers and citations to sources.
Topic 4 of 5 in Techniques to improve gen AI model output
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