Applications of Foundation Models
Choosing and using models
Amazon Bedrock provides managed access to foundation models from multiple providers through one API, with serverless scaling and no infrastructure management. Candidates should know Bedrock features: model choice, the Converse API for a consistent interface, Knowledge Bases for managed RAG, Agents for multi-step tasks that call APIs, Guardrails for filtering input and output, and provisioned throughput for predictable capacity. Bedrock supports text, image, and embedding models.
Application patterns
Match the pattern to the need. Summarization, classification, extraction, and generation are common text tasks. RAG grounds answers in a knowledge base. Agents orchestrate tools and actions. Embeddings power semantic search and recommendation. Multimodal models handle images plus text. Also know Amazon Q for business assistants and PartyRock for no-code experimentation, and that SageMaker hosts custom or open models you manage yourself.
Traps to expect
Questions conflate Bedrock with SageMaker: Bedrock is managed FM access via API; SageMaker is the full build-and-train platform. Another trap is assuming Bedrock trains models from scratch or that Knowledge Bases fine-tune the model. Watch for confusion between Guardrails (safety filtering) and Agents (task execution), and between on-demand and provisioned throughput.
Rule of thumb
Start with the simplest pattern: a single prompt for one-off text tasks, RAG when answers need your documents, an agent when the model must take actions, and fine-tuning only when prompting and RAG fall short.
Worked example
A team needs a chatbot that answers from a product manual and refuses off-topic requests. Use Bedrock Knowledge Bases for retrieval and Guardrails to block disallowed content, rather than training a new model.