Grounding Techniques: RAG, Google Search, and Sampling Settings

Grounding means connecting a model's output to a verifiable source of information, so the response is based on retrieved facts rather than only on what the model absorbed during training. A foundation model on its own generates answers from patterns learned before its knowledge cutoff. It has no built-in awareness of your company's documents, yesterday's news, or a product catalog that changed this morning. When it is asked about something outside its training data, it may produce a fluent but fabricated answer — a hallucination.

Grounding addresses this by giving the model reference material at the moment it answers. Instead of asking the model to recall, you ask it to read and then respond. The model's role shifts from being the source of truth to being a reasoning and language layer over a source of truth you control. This is why grounding is one of the Google Cloud-recommended practices for overcoming foundation model limitations, alongside prompt engineering, fine-tuning, and human-in-the-loop review.

For a business leader, the value is straightforward: grounded answers can cite where they came from, they stay current as the underlying data changes, and they reflect your organization's own information rather than a generic view of the world. That combination — accuracy, freshness, and traceability — is what makes gen AI dependable enough for customer-facing and decision-supporting work.

Grounding data comes from three distinct sources, and the exam expects you to tell them apart. First-party data is data your organization owns and generates: internal documents, product manuals, support tickets, HR policies, and customer records. Third-party data comes from external providers or partners: licensed industry reports, market data feeds, or partner catalogs — external information your organization acquires but does not create. World data is broad public knowledge, such as the information available through Google Search: news, public websites, and general facts about the world.

The distinction matters because each source answers a different business question. If a customer asks about your refund policy, only first-party data holds the answer. If an analyst asks about industry benchmarks, third-party data is the right source. If a user asks about something that happened last week, world data — fresh public information — is the only source that can be current enough.

Retrieval-augmented generation (RAG) is the technique that makes grounding work at scale: when a user asks a question, the system first retrieves the most relevant passages from a knowledge source, then passes those passages to the model along with the question, and the model generates its answer from the retrieved material. Retrieval happens at query time, every time — the knowledge base can change daily and the answers change with it, without retraining or fine-tuning the model.

RAG changes the generated output in several concrete ways. Answers become more accurate and specific, because the model is working from actual source text instead of a statistical impression of it. Answers become current, because the retrieved documents reflect today's state of the knowledge base, not the model's training cutoff. Answers become attributable, because the system knows which documents were retrieved and can cite them, letting users verify claims. And hallucinations drop sharply, because the model is instructed to answer from the provided context — and can say it does not know when the context contains no answer.

Compare this with fine-tuning, which bakes knowledge into the model itself. Fine-tuning suits stable knowledge, style, and behavior; RAG suits knowledge that changes, needs citations, or is too large and access-controlled to embed in a model. For most enterprise question-answering use cases, RAG is the faster, cheaper, and more governable path, which is why Google Cloud offers it both pre-built and as APIs.

Google Cloud provides three grounding offerings, each aimed at a different balance of speed, control, and data source. Pre-built RAG with Agent Search is the out-of-the-box option: you point it at your enterprise content — documents, sites, structured data — and it handles the entire retrieval pipeline for you, from ingesting and indexing content to retrieving relevant passages and grounding responses with citations. It uses Google-quality search technology over your first-party data, and it is the fastest route to a grounded enterprise assistant because there is no retrieval infrastructure to design.

RAG APIs serve teams that need to build a custom retrieval pipeline. Instead of one managed black box, developers get component APIs for the stages of RAG — processing documents, generating embeddings, ranking retrieved results, and grounding generation on them. This suits organizations with specific requirements the pre-built path cannot express: a specialized corpus, custom chunking or ranking logic, or integration into an existing application architecture. The trade-off is classic build-versus-buy: more control and flexibility in exchange for more engineering effort.

Grounding with Google Search connects the model to world data. When enabled, the model can ground its response in fresh Google Search results, which addresses the knowledge cutoff directly: questions about recent events, current prices, or newly published public information get answers based on what the web says now, with source links. It is the right choice when the facts you need are public and time-sensitive rather than proprietary.

These offerings combine naturally. A customer-service agent might ground product and policy answers in first-party data through Agent Search while using grounding with Google Search for public, fast-moving questions. The exam signal to watch for: enterprise documents, minimal build effort points to pre-built RAG with Agent Search; custom pipeline control points to RAG APIs; fresh public information points to grounding with Google Search.

Consider a retail bank that wants a gen AI assistant to answer customer questions about its accounts, fees, and services. An ungrounded model would be unusable here: it might state a fee schedule from its training data that is years out of date, or invent a policy that never existed — an unacceptable compliance and trust risk in a regulated industry.

The bank grounds the assistant in its first-party data — current product terms, fee schedules, and policy documents — using pre-built RAG with Agent Search, because the content is standard enterprise documentation and speed to deployment matters. Every answer now reflects the current published documents and carries citations a support agent or auditor can check. When a document is updated, the next answer reflects the change automatically, with no model retraining.

Later, the bank adds licensed third-party market data so the assistant can answer comparative questions about industry-standard rates, and enables grounding with Google Search for public, time-sensitive questions such as current regulatory announcements. Each data source maps to a category of question the business needs answered — which is exactly how the exam frames grounding decisions: start from what the user needs to know, then choose the source that authoritatively holds that knowledge.

Grounding controls what a model knows when it answers; sampling parameters and settings control how it generates the answer. A language model produces output one token at a time — a token being a small chunk of text, roughly part of a word — and at each step it chooses from many possible next tokens, each with a probability. The sampling parameters shape that choice, and the settings around them constrain the response as a whole.

Temperature is the parameter most tested. At low temperature the model strongly favors the most probable tokens, producing predictable, repeatable answers — right for factual Q&A, summarization, and anything grounded, where you want the model to stick to its sources. At high temperature, probability differences flatten and less likely tokens get chosen more often — useful for brainstorming, marketing copy, and creative variation. Top-p works alongside temperature by trimming the candidate list itself: with a low top-p the model considers only a handful of high-probability tokens; with a high top-p it considers a wider pool.

Safety settings let you configure how strictly the model filters potentially harmful content. Google Cloud's models assess candidate responses against harm categories, and safety settings define the blocking threshold for each category. A consumer-facing brand assistant would run strict settings; an internal moderation-review tool might need more permissive ones to examine flagged content at all. The business point is that safety behavior is configurable per application, not fixed — you tune it to the audience and risk profile of each use case.

Output length caps the number of tokens the model may generate in a single response. This is both a cost control and a product decision: gen AI usage is priced by tokens, so a chat widget that needs two-sentence answers should not permit thousand-token responses. Token count more broadly is the unit in which context and cost are measured — longer prompts, larger retrieved contexts, and longer answers all consume more tokens, so leaders planning gen AI budgets think in tokens the way they think in compute hours elsewhere.

These settings work together with grounding, not instead of it. A well-designed grounded assistant typically pairs retrieval over trusted data with low temperature, appropriate safety settings, and an output length matched to the surface it serves — each control addressing a different dimension of response quality: factual basis, consistency, safety, and size.

Choosing correctly starts with two questions: where does the authoritative knowledge live, and what generation behavior does the use case demand? The knowledge question selects the grounding source — first-party enterprise data for your own documents and records, third-party data for licensed external information, world data via Google Search for fresh public facts. The behavior question sets the sampling parameters — low temperature and constrained output for factual, consistent answers; higher temperature and looser limits for creative work.

The offering follows from effort and control. Pre-built RAG with Agent Search is the default for grounding on enterprise content quickly; RAG APIs are for teams building a custom retrieval pipeline with specific requirements; grounding with Google Search is for currency beyond the knowledge cutoff. None of these requires retraining a model, which is precisely their business appeal: grounding decouples the model from the knowledge, so the knowledge can change daily while the model stays the same.

Exam questions in this objective are scenario-driven. Expect a described business need — reduce hallucinations in a support bot, answer questions about last week's announcement, make a legal-drafting assistant less creative — and four plausible responses. Anchor on the vocabulary: hallucination and verifiable sources point to grounding; current public events point to grounding with Google Search; proprietary documents point to first-party data and RAG; consistency versus creativity points to temperature and top-p; response size and cost point to token count and output length; harmful-content filtering points to safety settings.