So you can use a few generations of GenAI applications

While generative intelligence (generative AI or GenAI) is based on increasing skills, language models can be developed and thus LLMs (Large Language Models) can be generated. These models will have a larger size than the expected consequences, but it may be that there is a right to general data. This can never be a problem again and can no longer be successful. Completely avoid the data security, which can be in the periods of the GDPR and other regulations of central meaning. In this context, a technique comes into play, which is RAG (Retrieval-Augmented Generation). This Beitrag sets the Retrieval Method and Advantages.

The following is a basic KI approach which ChatGPT depends on different factors. Here, if you look at all the parameters, this is one of the ways you can use the data base. So you can play ChatGPT 4 with more than 170 billion parameters, and the quality of your AI application was finally clear. Vergleicht man das beisplayelsweise with Google PaLM 2 with signals from 10 to 300 Billion Parameters, quickly become clear, where OpenAI with ChatGPT is ready for GenAI-Rennen.

If you choose Enterprise, you can perform your own GenAI procurement bzw. Development is not an option, but it is certainly not unreasonable on the size of the underlying data base and. Play a number of other aspects in a mountain-ordered role. If you would accept the actual data protection, the costs and a higher value of your data, the Data Protection involvement.

It’s the Stelle, a new technical name Retrieval Augmented Generation (RAG) ins Spiel komt. RAG is very ground-oriented, pre-trained LLMs are together with other data, which are placed in external databases, documents and other sources of application. A new foundation for the business application has emerged, both of which can be used. A combination with one of the best results of the Large Language Model (LLM) and the external data, via the RAG Framework can be used. These can be merged with a view or another external database. If you know eight, it is a matter of action and business.

RAG-basierte GenAI-Anwendungen weisen zahlreiche Vorteile auf

If you use the standard LLMs, the results of the standard LLMs have become larger. To the essential values:

1. Anstatt prepares existing Large Language Models for their own operations and arrangements, sets out the Einsatz of RAG-based Frameworks in a German language cheap alternative dar. Obtaining paint can no longer have the know-how that allows LLMs and their businesses to benefit.

2. AI experts find that LLMs have a “violent experience” on the market, even if they have a bad or bad experience. If the data base is displayed, this or the fragment of the application (of the following prompt) that the correct answer can be financed. Per RAG, Sprachmodelle liefer daher expected results. The fact that we were on the path that was based on the integrated database was the processing in the GenAI supplement.

3. The data from a RAG-based GenAI guarantee provides a guarantee higher actualitythat it is necessary to integrate new numbers and recognitions in Echtzeit. If so, it is possible to create an overview of the relevant web pages and social media feeds – for a high degree of topicality.

4. The Privacy Policy nimmt Based on a RAG implementation deutlich zu. So if you want to find the data on a server in your own search center, you can back up that data to a foreign storage – so that the data centers that data centers can use can work very efficiently.

Goods, maintenance work and more

RAG-based GenAI-Anwendungen comes in more and more branches of Einsatz. That was heard under the other Online trade and fruitful businesses. So if you do by RAG implementation tips shopping history and market trends almost in real time analyses, and a helper of such machines and social media on Facebook and Threads, on the Webanwender:innen in your tracks and shopping preferences behind.

If you are looking for a basis of RAG-based GenAI applications, you can make a plan and make plans. There is an exact description of its einsatz-commenden-maschinen, which in the most likely versügbaren LLMs are not or no longer available.

In a second part I can rely on the previous Use Cases in Sachen RAG-GenAI applications and tools. So: stay tuned…

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