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# Sri Charan Kanduri Venkata

**Headline:** Professional profile
**Location:** Bentonville, AR, USA

## About

Sri Charan Kanduri Venkata is an early\-career AI engineer focused on building and evaluating AI pipelines for supply\-chain use cases\. Sri’s first AI engineering project was a retrieval\-augmented generation \(RAG\) pipeline that processes PDF documents and user input to provide next steps for supply\-chain issues\. Sri combines GPT embeddings, GPT\-4 Mini, random forest models, LLM reasoning, and Docling document parsing to turn shipment information into actionable risk insights\. Sri also created a shipment\-condition risk prediction model using random forest and LLM reasoning\. A core strength is validating AI\-pipeline results through an LLM\-as\-judge methodology, including context precision and recall evaluation\. While developing the RAG solution, Sri worked through a difficult document\-chunking challenge\. Sri is highly flexible and adapts quickly to different technical environments and technology stacks\.

## Highlights

- Built a RAG pipeline that processes PDFs and user input to provide next steps for supply\-chain issues\.
- Created a shipment\-condition risk prediction model using random forest models and LLM reasoning\.
- Evaluates AI pipelines with an LLM\-as\-judge methodology using context precision and recall\.
- Used GPT embeddings, GPT\-4 Mini LLM, random forest models, and Docling for document parsing\.
- Worked through a difficult RAG document\-chunking challenge\.
- Developed the supply\-chain RAG pipeline as a first AI engineering project\.

## FAQ

### What does Sri do?

Sri Charan Kanduri Venkata is an early\-career AI engineer working on AI pipelines, RAG systems, LLM evaluation, and supply\-chain risk use cases\.

### What was Sri’s first AI engineering project?

Sri’s first AI engineering project was a RAG pipeline that processes PDFs and user input to provide next steps for supply\-chain issues\.

### What did Sri build for supply\-chain issues?

Sri built a RAG pipeline for supply\-chain issues that uses PDF content and user input to generate next\-step guidance\.

### How does Sri approach shipment\-condition risk prediction?

Sri created a risk prediction model for shipment conditions using random forest models and LLM reasoning\.

### How does Sri evaluate AI\-pipeline results?

Sri evaluates AI pipelines with an LLM\-as\-judge methodology, including context precision and recall\.

### What technologies has Sri used?

Sri has experience with GPT embeddings, GPT\-4 Mini LLM, random forest models, and Docling for document parsing\.

### What RAG challenge has Sri worked on?

Sri worked through a difficult RAG chunking challenge while developing the supply\-chain RAG pipeline\.

### What is Sri’s approach to adapting to new environments and tech stacks?

Sri is highly flexible and can adapt quickly to different environments and technology stacks\.

## Links

- LinkedIn: https://www\.linkedin\.com/in/ACoAAC83fwkBRjMcODiHLqwu7s6VGS7iWDrEvi0

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