SpringTutorials
    Spring AI Tutorials
    Tutorial 07

    Multimodal AI & Response Evaluation

    Work with text, audio, images, evaluate LLM responses, and build voice/image generation capabilities with Spring AI

    What You'll Learn

    7 sections on multimodal AI and response evaluation

    Multimodal Capabilities in Spring AI

    Evaluating LLM Responses

    Spring AI Evaluator in Practice

    Evaluating RAG-Based Applications

    Runtime Validation and Resilience

    Voice and Speech Capabilities

    Image Generation with Spring AI

    1
    Multimodal Capabilities in Spring AI

    1.1

    Introduction to Multimodal AI Tasks

    Explore the world of AI that processes multiple modalities: text, audio, images, and more.

    1.2

    Working with Text, Audio, and Images Using Spring AI

    Learn how Spring AI provides unified abstractions for different content types.

    Coming Soon

    Detailed content with code examples and practical demos is being prepared.

    2
    Evaluating LLM Responses

    2.1

    Why AI Output Evaluation Matters

    Understand the critical importance of validating AI-generated content in production.

    2.2

    Assessing Accuracy, Relevance, and Hallucinations

    Learn key metrics for measuring the quality and reliability of LLM outputs.

    Coming Soon

    Detailed content with code examples and practical demos is being prepared.

    3
    Spring AI Evaluator in Practice

    3.1

    Using Spring AI Evaluators to Score Model Responses

    Implement built-in evaluators to automatically assess AI response quality.

    3.2

    Evaluation Strategies for Production AI Systems

    Best practices for continuous evaluation in live AI applications.

    Coming Soon

    Detailed content with code examples and practical demos is being prepared.

    4
    Evaluating RAG-Based Applications

    4.1

    Applying Spring AI Evaluator in a RAG Pipeline

    Evaluate both retrieval quality and generation accuracy in RAG systems.

    4.2

    Measuring Retrieval Quality and Answer Faithfulness

    Key metrics for assessing whether responses are grounded in retrieved documents.

    Coming Soon

    Detailed content with code examples and practical demos is being prepared.

    5
    Runtime Validation and Resilience

    5.1

    Validating AI Responses at Runtime

    Implement real-time validation to catch problematic AI outputs before they reach users.

    5.2

    Combining Spring AI Evaluator with Spring Retry

    Build resilient AI pipelines that automatically retry on low-quality responses.

    5.3

    Handling Low-Confidence or Invalid AI Outputs

    Strategies for gracefully handling AI failures and edge cases.

    Coming Soon

    Detailed content with code examples and practical demos is being prepared.

    6
    Voice and Speech Capabilities

    6.1

    Speech-to-Text: Audio Transcription with Spring AI

    Convert audio input to text using Spring AI's transcription capabilities.

    6.2

    Text-to-Speech: Generating Natural Voices Using SpeechModel

    Create lifelike speech output from text using AI voice synthesis.

    Coming Soon

    Detailed content with code examples and practical demos is being prepared.

    7
    Image Generation with Spring AI

    7.1

    From Prompt to Image Using ImageModel

    Generate images from text descriptions using Spring AI's ImageModel abstraction.

    7.2

    Building Visual Content with Generative AI

    Create production-ready image generation features in your Spring applications.

    Coming Soon

    Detailed content with code examples and practical demos is being prepared.

    What You'll Master

    Multimodal AI

    Text, audio, and image processing

    Response Evaluation

    Accuracy, relevance, and hallucination detection

    Spring AI Evaluator

    Built-in scoring and production strategies

    RAG Evaluation

    Retrieval quality and answer faithfulness

    Voice Capabilities

    Speech-to-text and text-to-speech

    Image Generation

    Prompt-to-image with ImageModel

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