Generative AI with Amazon Bedrock Training introduces learners to AWS capabilities for developing intelligent applications with foundation models. Participants learn model selection, prompt engineering, embeddings, Retrieval-Augmented Generation (RAG), knowledge bases, agents, guardrails and model customization. The program also covers application integration, security, responsible AI and deployment considerations. Through practical exercises and real-world scenarios, learners develop the skills required to design scalable generative AI solutions using Amazon Bedrock across customer service, content generation, analytics and enterprise automation use cases.
Intermediate Level
1. What is Amazon Bedrock?
Answer: Amazon Bedrock is a fully managed AWS service that provides access to foundation models from multiple AI providers through APIs. It enables developers to build generative AI applications without managing underlying model infrastructure.
2. What are foundation models in Amazon Bedrock?
Answer: Foundation models are large pretrained AI models capable of performing tasks such as text generation, summarization, classification, question answering and image generation. Amazon Bedrock provides access to multiple foundation models through a unified service.
3. Why use Amazon Bedrock instead of hosting an AI model independently?
Answer: Bedrock reduces infrastructure management requirements and provides managed access to foundation models. It also offers AWS security, scalability, model choice and integration capabilities that simplify enterprise generative AI development.
4. What is prompt engineering?
Answer: Prompt engineering is the process of designing effective instructions for an AI model to produce accurate and relevant outputs. It can involve role definition, context, examples, constraints and output formatting.
5. What is Retrieval-Augmented Generation (RAG)?
Answer: RAG combines information retrieval with text generation. Relevant information is retrieved from a knowledge source and provided to the foundation model as context, helping generate responses grounded in organizational data.
6. What is an embedding?
Answer: An embedding converts text or other information into numerical vectors representing semantic meaning. These vectors can be compared to identify information that is conceptually similar.
7. What is a vector database?
Answer: A vector database stores and searches numerical embeddings efficiently. In generative AI applications, it can help retrieve documents or content that are semantically relevant to a user's query.
8. What are Amazon Bedrock Knowledge Bases?
Answer: Knowledge Bases provide a managed capability for connecting foundation models with enterprise data. They can support retrieval workflows where relevant information is obtained from connected data sources before generating responses.
9. What are Amazon Bedrock Agents?
Answer: Bedrock Agents help generative AI applications perform tasks by orchestrating interactions with foundation models, APIs, data sources and business systems. They can use actions to execute operations based on user requests.
10. What are Guardrails in Amazon Bedrock?
Answer: Guardrails help control model inputs and outputs according to defined policies. They can help filter inappropriate content, manage denied topics and improve the responsible use of generative AI applications.
11. What is model inference?
Answer: Model inference is the process of sending input to a trained model and receiving an output. In Bedrock, applications can invoke supported foundation models to generate responses based on prompts.
12. What is temperature in generative AI?
Answer: Temperature controls the randomness of model responses. Lower values generally produce more predictable outputs while higher values can produce more diverse and creative responses.
13. What is RAG useful for in enterprise applications?
Answer: RAG is useful when applications need to answer questions using frequently changing or organization-specific information that may not be contained in the foundation model's original training data.
14. How can Amazon Bedrock integrate with AWS applications?
Answer: Bedrock can be integrated with AWS services and application components through APIs and SDKs. It can work with services such as Amazon S3, AWS Lambda, databases and application workloads.
15. What security considerations should be considered when using Bedrock?
Answer: Organizations should consider IAM permissions, encryption, data access controls, logging, network security, sensitive-data handling and responsible AI policies when implementing Bedrock applications.
Advanced Level
1. How would you design an enterprise RAG architecture using Amazon Bedrock?
Answer: A typical architecture can use Amazon S3 or another enterprise repository as the data source, a Bedrock Knowledge Base for ingestion and retrieval, embeddings for semantic search and a foundation model for response generation. Access controls, monitoring, guardrails and application-level security should be incorporated.
2. How do you reduce hallucinations in a Bedrock application?
Answer: Hallucinations can be reduced through RAG, high-quality source documents, effective prompts, appropriate model selection, lower-temperature settings when suitable, output validation and guardrails. Applications should also clearly distinguish retrieved facts from generated content.
3. How would you choose a foundation model in Amazon Bedrock?
Answer: Model selection should consider task requirements, response quality, latency, context-window requirements, supported capabilities, cost, throughput and regional availability. Benchmarking candidate models against representative business workloads is preferable to choosing solely by model size.
4. What is the difference between RAG and fine-tuning?
Answer: RAG supplies external information to the model during inference, making it useful for dynamic or proprietary knowledge. Fine-tuning modifies model behavior using training examples and is more suitable when the goal is to adapt behavior, style or task performance rather than simply provide changing factual information.
5. When would you use fine-tuning instead of RAG?
Answer: Fine-tuning may be appropriate when consistent behavior, terminology, formatting or task-specific performance is required. RAG is generally more appropriate when the primary requirement is accessing frequently changing or private knowledge.
6. How would you optimize the cost of a Bedrock generative AI application?
Answer: Cost optimization can involve selecting an appropriately sized model, reducing unnecessary prompt tokens, optimizing retrieved context, caching suitable responses, controlling inference frequency and monitoring token consumption. Workloads should be benchmarked for both cost and quality.
7. How can you improve RAG retrieval quality?
Answer: Retrieval quality can be improved through appropriate document preprocessing, effective chunking, metadata filtering, high-quality embeddings, relevant retrieval parameters and potentially reranking. Evaluation should use representative queries and expected source documents.
8. What is prompt injection and how can you mitigate it?
Answer: Prompt injection occurs when malicious or unintended instructions manipulate an AI application into ignoring intended controls. Mitigation includes input validation, prompt isolation, least-privilege permissions, guardrails, controlled tool access and validation of retrieved or generated content.
9. How would you secure a Bedrock application using AWS IAM?
Answer: IAM should follow the principle of least privilege. Applications should receive only the permissions required to invoke models or access specific AWS resources. Separate roles should be used where appropriate for applications, services and administrative operations.
10. How would you evaluate a production RAG application?
Answer: Evaluation should measure retrieval relevance, factual accuracy, groundedness, response quality, latency, cost and safety. A representative evaluation dataset can be used to compare retrieval and generation performance before and after system changes.
11. How do Bedrock Agents interact with external systems?
Answer: Agents can use defined actions to interact with external business functionality. An application can expose controlled operations through APIs or action groups, allowing the agent to determine when an operation is required and pass structured parameters to the corresponding backend.
12. How would you prevent an AI agent from performing unauthorized actions?
Answer: Use least-privilege IAM permissions, tightly defined action interfaces, input validation, authorization checks and explicit business rules. High-risk operations should also include approval or human-in-the-loop controls rather than allowing unrestricted autonomous execution.
13. How would you design a scalable Bedrock application?
Answer: The architecture should use stateless application components where possible, appropriate AWS compute and API services, controlled model invocation, scalable data stores and monitoring. Concurrency, throttling, latency and model quotas should be considered during capacity planning.
14. What is the role of guardrails in responsible AI architecture?
Answer: Guardrails provide policy-based controls around model interactions. They can help detect or block undesirable content and define boundaries for model behavior. They should complement, rather than replace, application security, authorization and data-governance controls.
15. How would you troubleshoot poor responses from a Bedrock-based AI application?
Answer: First identify whether the problem originates from the prompt, model selection, retrieval quality, source data, context construction or application logic. For RAG systems, inspect retrieved documents and relevance. Then evaluate model parameters and prompts using controlled test cases before making production changes.
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