Databricks Generative AI Engineer Associate

Module 1: Generative AI and LLM Fundamentals

• Introduction to generative AI and large language models.
• LLM capabilities, limitations, and use cases.
• Foundation models and model selection.
• Responsible AI considerations for GenAI applications.

Module 2: Prompt Engineering

• Prompt design principles.
• Zero-shot, few-shot, and structured prompting.
• Prompt optimization and evaluation techniques.
• Managing hallucinations and improving output quality.

Module 3: Retrieval-Augmented Generation (RAG)

• RAG architecture and workflow design.
• Document ingestion and chunking strategies.
• Embeddings and vector search concepts.
• Improving relevance and grounding responses.

Module 4: Databricks GenAI Tools and Workflows

• Using Databricks for GenAI application development.
• Managing data pipelines for AI applications.
• Model serving and endpoint concepts.
• Integrating AI workflows into business applications.

Module 5: Evaluation, Monitoring and Optimization

• Evaluating GenAI application quality.
• Monitoring model outputs and application performance.
• Testing prompts, retrieval quality, and response accuracy.
• Continuous improvement for GenAI systems.

Module 6: Governance, Security and Responsible AI

• AI governance and access control concepts.
• Data security and privacy considerations.
• Responsible AI and compliance practices.
• Managing risks in production GenAI applications.

Training Eligibility

• There are no mandatory prerequisites to attend the Databricks Generative AI Engineer Associate training program.
• Professionals interested in AI engineering, generative AI, data intelligence, and modern application development can enroll.

Recommended Knowledge

• Basic understanding of Python or programming concepts.
• Basic knowledge of cloud, data, and machine learning concepts.
• Awareness of large language models and generative AI concepts is helpful.
• Prior Databricks experience is useful but not mandatory for training.