Genai

Diagnose AI agents that repeatedly call tools, revisit the same state, or fail to converge on a result.

AI systems that use models, tools, context, and control logic to complete tasks.

A practical, connected roadmap from Python and ML foundations to LLMs, RAG, agents, evaluation, production AI, and cloud architecture.

Understand the current distinction between Bedrock Agents Classic and Amazon Bedrock AgentCore when designing agentic applications.

Core production concerns for building generative AI applications with Amazon Bedrock.

How embeddings represent data as vectors and how similarity search supports retrieval systems.

Evaluate retrieval, generation, safety, reliability, latency, and cost instead of relying on a single model score.

Core security controls for LLM, RAG, and agentic AI applications.

A design case for tool-using AI agents with bounded execution, authorization, observability, and safe failure handling.

A practical framework for evaluating LLM, RAG, and agent systems before and after production release.

A requirements-first architecture pattern for designing a production retrieval-augmented generation platform.

A practical framework for writing, testing, versioning, and evaluating prompts in production AI systems.

Diagnose unsupported RAG answers by separating retrieval, context construction, model behavior, and evaluation failures.

A production-oriented introduction to retrieving external information and using it as context for generative AI responses.