Prompt Architecture and NLP on Amazon Bedrock

Overview

Learn to design production prompt architectures and build advanced NLP tools on Amazon Bedrock. You will explore the token processing lifecycle from raw text input through tokenization to model output, then design reusable prompt templates with variable placeholders, version tracking, and A/B testing through Bedrock prompt management. The course covers prompt-as-code workflows that integrate prompt lifecycle management with existing DevOps pipelines, including programmatic prompt creation and invocation via the AWS CLI. In the second module, you build advanced NLP implementations using Bedrock agents with chain-of-thought prompting and the five-whys analysis methodology for root cause investigation. You construct NLP agent pipelines that decompose complex language tasks into multi-stage processing workflows, integrate Amazon Transcribe for speech-to-text input layers, and build custom Rust NLP tools including SVGen for AI-powered SVG diagram generation and an Ollama-Bedrock bridge for hybrid local-cloud NLP deployment. By completing this course, you will be able to architect versioned prompt systems, implement chain-of-thought agent workflows, and build production NLP tools in Rust.

Syllabus

Prompt Architecture Fundamentals

Covers prompt, Bedrock, architecture, token, and processing.

Advanced NLP Implementation

Covers agents, NLP, advanced, analysis, and pipeline.

Capstone

Build a production NLP pipeline that applies structured prompt architecture principles to Amazon Bedrock, combining prompt management with versioning, chain-of-thought agent workflows, and custom Rust-based NLP tools. The system processes unstructured text through multiple pipeline stages, from tokenization analysis through agent-driven reasoning to SVG visualization of results.