Generative AI Workshop for Enterprise Use Cases (Gen AI)

We begin with a discovery session so we can tailor the workshop to align on business outcomes, success metrics, and data availability. From there we move through business understanding, data understanding, and data preparation stages, followed by iterative model prototyping, validation, and deployment planning. The engagement culminates in the delivery of a targeted generative AI capability (for example, document summarization, code generation, conversational agents, or content aggregation / retrieval) and an actionable plan to take the solution to production.

### Key features of the workshop include:

• Business-aligned use case selection and success metrics.
• Privacy and security-aware data exploration and transformation.
• Rapid prototyping using responsible generative AI patterns (guardrails, prompt engineering, and evaluation metrics).
• Deployment feasibility and cost / operation estimate for production.
• A preliminary demonstration showcasing the feasibility and potential effectiveness of a specific generative AI solution in a real-world scenario.

#### Deliverables

• A feasibility report that summarizes business impact, success metrics, technical feasibility, and recommended next steps.
• Foundation candidate generative AI model(s) and prototypes (notably prompt sets, fine-tune seeds, or retrieval-augmented generation pipelines) that form the basis of a production model.
• Roadmap and timeline to production, including recommended milestones and required engineering tasks.
• Technical architecture and operational requirements (compute, storage, networking, security controls).
• Dataset inventory and data transformation notes, including data sensitivity classification and recommended anonymization steps.
• Evaluation plan and benchmark results with clear acceptance criteria for moving to production.

#### AWS AI & ML Services Commonly Used in our Workshops

Below are AWS services we frequently use to build, prototype, and prepare generative AI solutions during the workshop. We’ll recommend the best mix based on your use case, scale, and compliance needs.

**Amazon SageMaker**

**Use:** Model training, hyperparameter tuning, hosted endpoints, and managed fine-tuning for many model architectures.

*Notes:* Good for end-to-end MLOps and model lifecycle management; integrates with data labeling and pipelines.

**Amazon Bedrock**

**Use:** Access to multiple managed foundational models (FMs) for text, embeddings, and multimodal use cases.

*Notes:* Ideal for rapid experimentation with LLMs without heavy infrastructure setup; supports foundation model selection and prompt orchestration.

**Amazon SageMaker JumpStart**

**Use:** Quick-start templates and pre-built solutions for common generative tasks.

*Notes:* Helps accelerate prototype development using proven patterns.

**Amazon Rekognition / Amazon Textract**

**Use:** Extract structured data from images and documents (Textract), and analyze images/video content (Rekognition).

*Notes:* Useful when the generative use case requires multimodal inputs or document understanding.

**Amazon Elasticsearch / OpenSearch Service**

**Use:** Indexing and fast retrieval for RAG systems and analytics.

*Notes:* Common component in retrieval pipelines and logging/observability.

**AWS Glue & AWS Data Pipeline**

**Use:** Data cataloging, ETL, and transformation for dataset preparation.

*Notes:* Useful where data needs cleaning, normalization, or joining from multiple sources.