# grounding Published articles for grounding. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Bringing Google Maps to Friendly Meals with Firebase AI Logic DevFeed: [Bringing Google Maps to Friendly Meals with Firebase AI Logic]() Original publisher: [Read original article]() Author: Marina Coelho Published: 2026-07-28T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [Google Maps](), [Android](), [Kotlin](), [Google](), [SDKs](), [Mobile](), [personalization](), [real-time](), [configuration]() Tags: [ai](), [ai-logic](), [android](), [build](), [configuration](), [features](), [firebase](), [firebase-ai-logic](), [gemini](), [google](), [google-maps](), [grounding](), [kotlin](), [mobile](), [personalization](), [prompt](), [real-time](), [sdks](), [time]() ### AI overview A Firebase team tutorial explains how to add a Store Finder feature to the Friendly Meals Android app using Grounding with Google Maps through Firebase AI Logic. The Kotlin implementation uses location-aware Gemini responses to provide nearby businesses, operational details, and geographic personalization. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Build intelligent Android apps: Cloud and hybrid inference DevFeed: [Build intelligent Android apps: Cloud and hybrid inference]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-07-21T13:00:00Z Content type: tutorial Language: en Sources: [Android Developers Blog]() Topics: [Android](), [Firebase](), [Artificial Intelligence](), [Inference](), [Cloud](), [ML Kit]() Tags: [ai](), [ai-models](), [android](), [cloud](), [firebase](), [firebase-ai-logic](), [gemini](), [grounding](), [hybrid-inference](), [inference](), [on-device]() ### AI overview This tutorial explains how to build cloud-hosted and hybrid AI features in Android apps with Firebase AI Logic. Using the Jetpacker app as an example, it covers web-grounded museum assistance, hybrid restaurant review drafting with Gemini Nano and cloud fallback, and custom-routed live translation for hotel support. ### Source excerpt Posted by Thomas Ezan, Jolanda Verhoef, Caren Chang, Senior Developer Relations Engineers, Android Developer Relations Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we explored how to build intelligent on-device features using Gemini Nano through ML Kit's Prompt API. In this post, we will look at how you can leverage Firebase AI Logic to build cloud-hosted and hybrid AI features: Grounding answers in real-world context Routing requests dynamically between cloud and local execution using hybrid inference Translating content with custom routing systems Sometimes a use case requires AI models with greater world knowledge, a much larger context window, or the ability to handle complex queries. In those scenarios, we can leverage cloud models. Other times, you want the best of both worlds: using hybrid inference to run on-device when available to lower costs, while falling back to the cloud to ensure compatibility for all devices. Cloud and hybrid features in Jetpacker: Museum assistant with web grounding, hybrid restaurant review drafting, and support chat featuring custom-routed live translation. Let's look at how we implemented three cloud and hybrid features in Jetpacker: a museum assistant with web grounding hybrid restaurant review drafting hotel support chat featuring custom-routed live translation. Use LLM grounding for up-to-date informationMuseum assistant chatbot with LLM grounding The Museum assistant is an interactive chatbot designed to help users plan their museum visits. It provides visitors with up-to-date details regarding specific exhibits, current opening hours, ticket pricing, and more. Museum assistant is a chatbot that answers questions, such as 'How can I get a ticket discount for Le Louvre?' When building AI features, getting the model to answer with fresh, accurate, and specific real-world info ## Doximity Ask DevFeed: [Doximity Ask]() Original publisher: [Read original article]() Author: Doximity Published: 2026-06-23T09:30:00Z Content type: article Language: en Sources: [Doximity]() Topics: [Artificial Intelligence](), [Retrieval-Augmented Generation](), [Hallucination detection]() Tags: [agentic](), [ai](), [grounding](), [hallucinations](), [retrieval-augmented-generation](), [review](), [verification]() ### AI overview The article explains how Doximity Ask approaches reliability in clinical AI. It describes a HIPAA-compliant clinical reference platform that combines retrieval-augmented generation, validated tools, a reasoning model, citation-grounded responses, automated verification, physician-authored evaluation, and production monitoring. It argues that reliability is a system property spanning retrieval, ranking, reasoning, tool use, verification, and evaluation. ### Source excerpt Medical AI has attracted confident claims: perfect scores, no hallucinations, a system that gracefully declines whenever the evidence runs out. But are these dimensions the right way to think about a tool that physicians use to make real-world decisions? For clinical AI, reliability is not just a question of whether an answer is correct. It is also a question of whether a physician can understand where the answer came from, evaluate the supporting evidence, and identify situations where uncertainty remains. How Doximity Ask Answers Clinical Questions Few domains face stakes as high as medical AI, where system performance can directly influence patient outcomes, clinical workflows, and healthcare professionals' decisions. Clinical reference requires traceable grounding, numerically exact doses and scores, and explicit handling of situations in which the available evidence is insufficient to support an answer. At the same time, healthcare providers often work under significant time constraints and have exceptionally high expectations for the performance and reliability of AI systems. Doximity Ask is a HIPAA compliant composite clinical reference platform for question answering at the point of care. It combines retrieval-augmented generation over a physician-governed evidence library, multi-turn agentic tool use through a fixed registry of validated calculators and structured lookups, and a post-trained reasoning model that orchestrates retrieval, tool calls, and citation-grounded generation. Automated verifiers, physician-authored rubrics, and in-product physician review form one evaluation loop that grades releases, defines training rewards, and monitors production. Reliability Is A System Property Over 150 health systems have purchased the Doximity Clinical AI Suite including 8 of the top 20 hospitals in the country, therefore reliability is paramount. The reliability of a clinical AI system depends on more than the capabilities of its underlying language model. Pro ## Proofs and Essays Are Paths: An LLM ↔ Prover Loop for Falsifying Hallucinations DevFeed: [Proofs and Essays Are Paths: An LLM ↔ Prover Loop for Falsifying Hallucinations]() Original publisher: [Read original article]() Published: 2026-04-30T00:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [Large Language Model](), [Automated reasoning](), [Graphs](), [Artificial Intelligence](), [Math and Logic]() Tags: [grounding](), [hallucinations](), [knowledge-graph](), [llm](), [reasoning](), [smt](), [verification](), [verify]() ### AI overview This essay proposes a bidirectional loop between a large language model and a theorem prover. LLM outputs become propositions and graph connections that the prover checks, while verification results feed back into the model. In this framework, hallucinations appear as knowledge-graph paths with edges that fail formal verification. ### Source excerpt If a proof is a path through a formal space and an essay is a path through a semantic one, can we close the loop -- translate LLM outputs into a theorem prover, build a knowledge graph of verifiable propositions, and use the gaps as a signal for hallucination? Notebook ideas from March 2024 expanding on the SMT-grounding piece. ## Data Is the Bottleneck in Many AI Projects DevFeed: [Data Is the Bottleneck in Many AI Projects]() Original publisher: [Read original article]() Author: Brian Jenney Published: 2026-02-07T16:25:26Z Content type: opinion Language: en Sources: [Brian Jenney]() Topics: [data](), [Web Scraping](), [Structured-data](), [Artificial Intelligence]() Tags: [ai](), [data](), [data-pipeline](), [grounding](), [web-scraping]() ### AI overview The article argues that data collection and preparation, rather than wiring together AI components, are often the main bottlenecks in AI projects. It discusses messy real-world data, data pipelines, vector databases, and web scraping as ways to ground useful AI outputs. ### Source excerpt This is the first Saturday I've had off in a few weeks. ## Grounding with Google Search in the Firebase AI Logic client SDKs DevFeed: [Grounding with Google Search in the Firebase AI Logic client SDKs]() Original publisher: [Read original article]() Author: Ankita Saxena; Daniel La Rocque Published: 2025-07-28T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google Search](), [SDKs](), [Artificial Intelligence](), [Mobile](), [Web]() Tags: [ai](), [ai-logic](), [android](), [firebase](), [flutter](), [gemini](), [google](), [google-search](), [grounding](), [ios](), [javascript](), [mobile](), [news](), [react](), [sdks](), [search](), [tutorials](), [unity](), [updates](), [vertex-ai](), [web]() ### AI overview Firebase announces Grounding with Google Search for its AI Logic client-side SDKs. The feature lets Gemini search current web content before generating responses, enabling mobile and web applications to use more up-to-date information. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Visual Salamandra: Pushing the Boundaries of Multimodal Understanding DevFeed: [Visual Salamandra: Pushing the Boundaries of Multimodal Understanding]() Original publisher: [Read original article]() Author: Iñigo Pikabea; Jaume Lozano Published: 2025-04-11T14:21:56Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [multimodal-ai](), [vlm](), [Artificial Intelligence](), [Large Language Model](), [datasets](), [Embeddings](), [mlp](), [AI Research]() Tags: [ai](), [blog](), [blog-post](), [datasets](), [embeddings](), [grounding](), [hugging-face](), [language](), [llm](), [mlp](), [models](), [multimodal](), [multimodal-ai](), [ocr](), [research](), [training](), [vision](), [vqa]() ### AI overview Visual Salamandra is a multilingual multimodal model built by extending the Salamandra Instructed 7B model with Google's SigLIP image encoder, an MLP projector, and late-fusion techniques. It processes text, images, and videos, with training focused on visual grounding, document understanding, mathematical reasoning, OCR, and European-language coverage. ### Source excerpt A Blog post by Language Technologies Laboratory @ Barcelona Supercomputing Center on Hugging Face ## High-Precision Responses with Genkit's Google Search Integration DevFeed: [High-Precision Responses with Genkit's Google Search Integration]() Original publisher: [Read original article]() Author: tanabee Published: 2024-10-21T18:37:56Z Content type: tutorial Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [Google Search](), [Google](), [Google Cloud Platform (GCP)](), [Artificial Intelligence]() Tags: [ai](), [ai-api](), [cloud](), [code](), [gemini](), [genkit](), [github](), [google](), [google-search](), [grounding](), [how-to](), [integration]() ### AI overview This tutorial explains how to integrate Google Search with Genkit 0.5.9 so applications can use current information, including details from specific domains, when generating responses. It covers enabling Vertex AI, configuring a Google Cloud project and local environment, initializing a Genkit project, and running the Genkit Developer UI. ### Source excerpt aGenkit With the release of Genkit version 0.5.9, it is now possible to leverage Google Search directly through Genkit. By using Google Search, Genkit can access the latest information, including niche details from specific domains, leading to more accurate responses. This article explains how to set up Google Search integration for Genkit. Enabling Vertex AI To use the Google Search feature, you need a Google Cloud project with Vertex AI enabled. Follow these steps to enable Vertex AI and link your local environment with Google Cloud: 1. In the Cloud console, Enable the Vertex AI API for your project. 2. Set environment variables: export GCLOUD_PROJECT= export GCLOUD_LOCATION=us-central1 3. Authenticate with gcloud: gcloud auth application-default loginInitializing a Genkit Project Next, initialize your Genkit project. % npm init -y % npm i -D genkit-cli % npm i genkit @genkit-ai/googleai @genkit-ai/vertexai % mkdir src && touch src/index.ts Paste the following initialization code into the src/index.ts file. import { genkit, z } from 'genkit'; import { vertexAI } from '@genkit-ai/vertexai'; import { gemini15Flash } from '@genkit-ai/vertexai'; const ai = genkit({ model: gemini15Flash, plugins: [vertexAI({ location: 'us-central1' })], }); export const mainFlow = ai.defineFlow( { name: 'mainFlow', inputSchema: z.string(), outputSchema: z.string(), }, async (prompt) => { const { text } = await ai.generate(prompt); return text; } ); ai.startFlowServer({ flows: [mainFlow] });Enabling Google Search To enable Google Search, specify `googleSearchRetrieval` with `withConfig` for the `gemini15Flash` model, as shown in the following code. const ai = genkit({ model: gemini15Flash.withConfig({ googleSearchRetrieval: {}}), plugins: [vertexAI({ location: 'us-central1' })], });Run locally Launch the Genkit Developer UI with the following command: % npx genkit start -- npx tsx --watch src/index.ts Try asking about the current weather in Tokyo. Ask about Tokyo's weat ## People-First Principles for Digital Wellbeing DevFeed: [People-First Principles for Digital Wellbeing]() Original publisher: [Read original article]() Published: 2020-09-17T13:00:00Z Content type: article Language: en Sources: [Material.io - Material Design]() Topics: [User experience (UX)](), [Google](), [digital]() Tags: [digital-wellbeing](), [google](), [grounding](), [ux]() ### AI overview The article previews the UX foundations behind Google's Digital Wellbeing Toolkit. ### Source excerpt A sneak peek at the UX foundations grounding Google's Digital Wellbeing Toolkit