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# Monisha Krishnamurthy

**Headline:** Professional profile
**Location:** Seattle, WA, USA

## About

Monisha Krishnamurthy builds and evaluates AI systems, with particular strength in retrieval\-augmented generation \(RAG\) search workflows designed to improve answer reliability\. Monisha applies defined evaluation sets and ground\-truth validation to test AI behavior rigorously, including validation through structured test cases\. Monisha has experience building RAG pipelines with LangChain, Pydantic, ChromaDB, and web\-search fallback mechanisms\. A key area of Monisha’s work is reducing hallucinations by using confidence scores to determine when a retrieval result should hand off to web search\. Monisha also works through the practical challenge of tuning confidence thresholds so that the system triggers fallback at the appropriate point\. This combines retrieval engineering, validation methodology, and iterative system improvement\. Monisha prefers an in\-person or hybrid work setup\.

## Highlights

- Built RAG pipelines using LangChain, Pydantic, ChromaDB, and web\-search fallback mechanisms\.
- Developed hallucination\-resistant RAG search approaches\.
- Used confidence scores to trigger web\-search fallback when retrieval results do not meet the required threshold\.
- Worked on complex retrieval\-to\-web handoff logic and confidence\-threshold tuning\.
- Applied defined evaluation sets and ground\-truth validation to AI\-system testing\.
- Validated AI results with rigorous test cases\.

## FAQ

### What does Monisha do?

Monisha Krishnamurthy works on AI\-system evaluation and retrieval\-augmented generation search pipelines\. Her work includes designing reliable retrieval workflows, validating outputs against ground truth, and using web\-search fallback when retrieval confidence is insufficient\.

### What are Monisha’s strongest areas of expertise?

Monisha’s strengths include rigorous AI testing methodology, defined evaluation sets, ground\-truth validation, RAG\-pipeline development, and hallucination\-resistant retrieval design\.

### What technologies has Monisha used for RAG pipelines?

Monisha has experience building RAG pipelines with LangChain, Pydantic, ChromaDB, and web\-search fallback mechanisms\.

### How does Monisha use confidence scores in AI search systems?

Monisha uses confidence scores to decide when a retrieval result should trigger a web\-search fallback\. This approach is intended to improve reliability when the retrieved information is not sufficiently confident\.

### What retrieval challenge has Monisha worked on?

Monisha has worked on the retrieval\-to\-web handoff problem: determining when a RAG system should rely on retrieved results and when it should fall back to web search\. This includes tuning the confidence threshold for the handoff\.

### How does Monisha test AI systems?

Monisha validates AI\-system results with rigorous test cases, defined evaluation sets, and ground\-truth validation\.

### What work setup does Monisha prefer?

Monisha prefers an in\-person or hybrid work setup\.

## Links

- LinkedIn: https://www\.linkedin\.com/in/ACoAAD2EEJ4BfE0FXjWIxeqlBH\-iL8xw7By8pF4

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