The cutting-edge large language model (LLM) APIs from OpenAI, Anthropic, and Google generally provide satisfactory answers. However, they significantly struggle with requests regarding current events. This limitation is due to their training set, which has a specific knowledge cut-off point. To address this issue, Google has recently unveiled an innovative feature in Google AI Studio and the Gemini API that allows users to integrate responses with real-time Google Search data.
The innovative Grounding with Google Search capability empowers developers to generate fresher and more precise responses from the Gemini LLMs. A standout aspect of this feature is that it includes grounding references (in-line links to sources) as well as Search Suggestions relevant to the contextual responses.
This new feature is compatible with all publicly available versions of the Gemini 1.5 models. However, it comes with a price tag of $35 for every 1,000 grounded queries. Developers interested in utilizing this functionality can navigate to the “Tools”section within Google AI Studio or activate the ‘google_search_retrieval’ tool within the API. As always, users can experiment with this Grounding functionality for free through Google AI Studio.
Google advises developers to leverage this feature in several key scenarios:
- Minimized hallucinations: Grounding contributes to the provision of more accurate information, enhancing the reliability of AI outputs.
- Access to current information: Grounding enables models to pull in real-time data, thereby ensuring AI responses remain relevant to a broader array of contexts.
- Increased trustworthiness and publisher traffic: By incorporating source links, grounding fosters transparency in AI applications, encouraging users to investigate the referenced content for more insights.
- More comprehensive data: By utilizing information from Google Search, grounding can enrich responses with additional context and detail.
When this feature is activated, upon receiving a user query, the Gemini model API will tap into Google’s search engine to retrieve the most recent information pertinent to the query, which will be processed by the Gemini model to deliver a more accurate and up-to-date response.
Images Credit: Neowin.net
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