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Structured output from documents using RAG LLM

Instructor based workshop for automatically extracting pre-defined reports from document using LLM and standard RAG.

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4.7

Rating

10 Hrs

Duration

Advanced

Level

10

Assignments

What you'll learn

How to parse PDF and word document to extract textual content
Prompt engineering and RAG for extracting value from document
How to create a RAG pipeline manually and get structured output from LLM

Who should take this course

Ideal for anyone with python programming and LLM knowledge
It is recommended to do the “LLM for beginners” training before joining this course

Skills you'll gain

PDF parsing, LLM, RAG, Prompt engineering, Python, Accuracy metrics, Output validation
LLM
RAG
Prompt engineering
Python
Accuracy metrics
Output validation

Course contents

Understanding the objective
Identify the fields that needs to be extracted

For each field create utility to automatically fetch context from source documents
Apply Doc Q&A for each field

Define the fields extraction flow
Create utility to validate field level output from LLM
Create the end-to-end extraction pipeline

Create app to upload document and down output
Add utility to export output as Json and Excel

Create functionality to automatically validate output from Ground truth

Developing LLM powered Chat Bot for customer support

Here we will built a context-enabled LLM-based chatbot that will answer all customer queries related to product and services.

LLM fine-tuning for medical domain

This workshop includes training or finetuning a more lightweight version of a LLM for specific tasks or ontology domains.

Applying GraphRAG in healthcare diagnostics

This workshop focuses on how to enhances the performance of RAG LLM by adding external knowledge base.