Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.

Learn to build event-driven workflows with complex branching, looping logic, and concurrent executions. Learn to build event-driven workflows with complex branching, looping logic, and concurrent executions.Build an event-driven agentic workflow that parses forms, retrieves relevant information from a source document using RAG, and returns answers to the form’s fields as structured outputs. Build an event-driven agentic workflow that parses forms, retrieves relevant information from a source document using RAG, and returns answers to the form’s fields as structured outputs.Incorporate human-in-the-loop to a document workflow to provide feedback to the agent through text and voice instructions. Incorporate human-in-the-loop to a document workflow to provide feedback to the agent through text and voice instructions._Learn to build Event-Driven Agentic Document Workflows, in this new course, built in partnership with LlamaIndex, and taught by Laurie Voss, VP of Developer Relations.Manually processing complex documents can be time-consuming, and error-prone. But what if an AI agent could automate it for you? Agentic document workflows are agent-based applications that you can use to automate an end-to-end document processing workflow. This course teaches you how to build an agentic document workflow, using an event-driven architecture, a design pattern that allows the agent to perform its tasks asynchronously and in parallel.You’ll design an event-driven agentic workflow that fills out a PDF form based on information from a source document. The agent will use RAG to retrieve relevant data from the source document, parse the form to identify the required fields, convert the blank spaces into questions, and send those questions to the RAG system. You’ll collaborate with the agent using a human-in-the-loop feedback approach through text and with your voice.In detail, you’ll: Understand the basic concepts of agentic document workflow, which consists of RAG, agents, and event-driven workflows.Build a series of LlamaIndex’s workflows that increase in complexity and involve branching, looping logic, and concurrent executions.Set up the agent’s RAG capability by parsing the source document, loading the extracted information into a vector store, and building a query engine on top of the store.Implement workflow steps that enable the agent to parse the form to be filled, turn the parsed information into simple questions, and use the questions to query the RAG pipeline.Incorporate human-in-the-loop into the workflow and ask the agent to re-answer when necessary to produce more accurate form responses.Add multimodal capability to the agent, allowing it to process spoken feedback.By the end of this course, you’ll have built an event-driven agentic workflow that fills out a document and responds to human feedback for more accurate form completion._Anyone who has basic Python knowledge and basic understanding of object-oriented programming, and wants to learn to build event-driven agent workflows!

Event-Driven Agentic Document Workflows

Learn how to deploy an LLM-based application into production using serverless technology. Learn to prompt and customize LLM responses with Amazon Bedrock.

Learn how to prompt and customize your LLM responses using Amazon Bedrock. Learn how to prompt and customize your LLM responses using Amazon Bedrock.Summarize audio conversations by first transcribing an audio file and passing the transcription to an LLM. Summarize audio conversations by first transcribing an audio file and passing the transcription to an LLM.Deploy an event-driven audio summarizer that runs as new audio files are uploaded using a serverless architecture. Deploy an event-driven audio summarizer that runs as new audio files are uploaded using a serverless architecture._In this course, you’ll learn how to deploy a large language model-based application into production using serverless technology.A serverless architecture enables you to quickly deploy your applications without the need to manage and scale the infrastructure that it runs on.You’ll learn to summarize audio files by pairing an LLM with an automatic speech recognition (ASR) model. Through hands-on exercises, you’ll build an event-driven system that automatically detects incoming customer inquiries, transcribes them with ASR and summarizes them with an LLM, using Amazon Bedrock. After course completion, you’ll know how to:Prompt an LLM and customize its responses using Amazon Bedrock. Convert audio recordings into written transcripts with Amazon Transcribe, and then summarize these transcripts using an LLM, Amazon Titan.Enable logging for all the calls you make to LLMs to help you maintain security, audit, and compliance standards.Deploy this audio summarizer as an event-driven serverless workflow using AWS Lambda.You’ll work with the Amazon Titan model, but in practice Amazon Bedrock allows you to use any model you prefer.Start building serverless LLM applications with Amazon Bedrock and deploy your apps in just days._Anyone who is familiar with Python, AWS services, and wants to learn to quickly deploy LLM apps with Amazon Bedrock. Special thanks to Vocareum for powering the labs provided in this course!

Event-Driven Agentic Document Workflows

Serverless LLM Amazon Bedrock