Traditionally accessing Artificial Intelligence Models, such as OpenAI's ChatGPT, has been the domain of programming languages Python and Javascript.

Not any more. Spring AI unlocks the power of Generative AI for Java developers.

The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity.

Spring AI provides support for all major Artificial Intelligence models, including:

• OpenAI

• Azure OpenAI

• Amazon Bedrock

• HuggingFace

• Ollama

• Google VertextAI (PaLM2 and Gemini)

• Mistral AI

• Antrhopic

• WatsonxAI

Spring AI also supports image generation AI models from OpenAI and Stability.

Retrieval Augmented Generation is an important use case for dealing with AI models. Spring AI includes robust support for all major Embedding Models and all popular vector databases.

No prior experience with AI is needed for this course.

You will start the course with a solid overview of what Artificial Intelligence is.

Next you'll have a hands on section to develop a RESTful API to ask OpenAI's ChatGPT questions. In this section, you will learn how we can instruct the model to return data in the format we desire.

Since no AI experience is required, the next section of the course builds upon what you learned with a formal look at Prompt Engineering. Prompt Engineering is a collection techniques to improve the quality and accuracy of responses from AI Models.

Retrieval Augmented Generation (RAG) is an important technique to provide Large Language Models with additional information required to the user's query to complete specialized tasks.

You will see how we can use RAG to develop AI experts to perform high specialized tasks.

The AI models are not just limited to working with textual information.

In this course you will also see how to use AI to create images, create audio files from text, and how to transcribe audio files to text.

Learn all this and more in Spring A:I Beginner to Guru. Enroll today.