GAIL question patterns

Ground the model with RAG or grounding on trusted sources, and add human review where stakes are high.

Hallucinations come from the model predicting plausible text with no source of truth. A bigger model does not remove that.

Grounding with Google Search for public facts, or RAG over fresh enterprise data.

The knowledge cutoff means training data is stale. Retrieval at question time brings current facts.

RAG over the company data, for example prebuilt RAG with Agent Search.

It reads your content at question time without retraining, and can cite sources.

Use RAG and update the documents.

Fine-tuning bakes in a snapshot and is costly to repeat. Updating a document store is cheap.

Few-shot prompting first, then fine-tune with labeled examples if still short.

Fine-tuning teaches style and behaviour, and prompting is the cheaper first step.

Keep a human in the loop to review before action.

Accountability stays with people, and review catches errors the model cannot.

Prompt engineering.

It is free and instant, with no data or training needed. Escalate to RAG and then fine-tuning.

Monitor drift, evaluate against KPIs and refresh data or model.

The world and inputs change, so models degrade without monitoring.

Zero-shot prompting.

The model already handles it, so examples add cost without benefit.

Few-shot prompting with several examples.

Examples show the exact pattern the model should copy.

Chain-of-thought prompting: ask it to reason step by step.

Showing intermediate steps improves reasoning accuracy.

ReAct prompting.

It alternates reasoning and acting with observations.

Prompt chaining.

Each prompt's output feeds the next, which keeps each step focused.

Role prompting.

A stated role sets tone, depth and viewpoint.

Low temperature.

Low temperature makes output focused and consistent.

Higher temperature or top-p.

More randomness gives more varied output.

Raise the max output token limit.

The length cap stopped generation.

Tighten safety settings and filters.

Safety settings block harm categories. Temperature does not.

Gemini Enterprise.

It is the workplace agent platform with multimodal search, connectors and a no-code agent builder.

Gemini for Google Workspace.

It adds drafting, summarising and notes where people already work.

A Gem in the Gemini app.

Gems are custom versions of Gemini with saved instructions.

Conversational Agents in Customer Engagement Suite.

It builds customer-facing agents with generative answers grounded in your data.

Agent Assist.

It supports the person during the conversation.

Conversational Insights.

It analyzes conversations at scale.

Agent Search.

It gives Google-quality search and RAG over your content without building a pipeline.

Agent Platform with Model Garden.

One place for model choice, customisation, deployment and controls.

AutoML on Agent Platform.

It trains models without model-building code.

Google AI Studio.

It is the fastest, lightweight route. Move to Agent Studio for enterprise controls.

Document AI API.

A pre-built API built for documents, no model building needed.

Gemma.

Gemma models are open weights and can be hosted and tuned by the customer.

Imagen for images, Veo for video.

Each is Google's model for that output type.

Gemini.

It is natively multimodal with a large context window.

Extension.

Extensions execute on the agent side.

Function.

Functions run client-side, giving the developer control over security and limits.

Data store.

Data stores give retrieval access to your content.

Pre-built RAG with Agent Search.

Managed indexing, retrieval and answers.

RAG APIs.

They are building blocks for custom RAG.

A prebuilt application or agent.

Higher layers of the stack give the fastest time to value with least skill.

Pick a high-value, low-risk use case, pilot with metrics, then scale.

Staged rollout proves value and limits risk.

Set a baseline, then track KPIs such as ROI, time saved, quality and satisfaction.

Without a baseline you cannot show change.

Apply Google's Secure AI Framework (SAIF).

It provides shared principles for securing AI end to end.

IAM with least privilege.

IAM defines who can do what on which resource.

Security Command Center.

It finds vulnerabilities and misconfigurations across Google Cloud.

Prompt injection, countered by input and output screening and least privilege.

Untrusted text is treated as instructions.

Pseudonymization.

Anonymization cannot be reversed. Pseudonymized data still counts as personal data.

Improve data representativeness, test for bias across groups and keep monitoring.

Bias mostly comes from data and design.

Explainability and accountability: sources, logs, documentation and named owners.

People remain responsible for outcomes.

Transparency.

Responsible AI means disclosing AI use and limits.