Annie Surla

Annie Surla is a Developer Advocate Engineer at NVIDIA responsible for developing and presenting a wide range of deep learning software products. She comes with experience working in deep learning applications including vision and NLP. She holds a master’s degree in Engineering Management from Duke University.
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Posts by Annie Surla

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Generative AI May 23, 2025

An Easy Introduction to LLM Reasoning, AI Agents, and Test Time Scaling

Agents have been the primary drivers of applying large language models (LLMs) to solve complex problems. Since AutoGPT in 2023, various techniques have been... 10 MIN READ
Generative AI Mar 06, 2025

How Using a Reranking Microservice Can Improve Accuracy and Costs of Information Retrieval

Applications requiring high-performance information retrieval span a wide range of domains, including search engines, knowledge management systems, AI agents,... 8 MIN READ
Generative AI Dec 16, 2024

An Easy Introduction to Multimodal Retrieval-Augmented Generation for Video and Audio

Building a multimodal retrieval-augmented generation (RAG) system is challenging. The difficulty comes from capturing and indexing information from across... 12 MIN READ
Generative AI Oct 28, 2024

An Introduction to Model Merging for LLMs

One challenge organizations face when customizing large language models (LLMs) is the need to run multiple experiments, which produces only one useful model.... 10 MIN READ
Decorative image of a person looking at a laptop with an overlay of the NVIDIA NIM logo.
Generative AI Aug 28, 2024

Build an Enterprise-Scale Multimodal PDF Data Extraction Pipeline with an NVIDIA AI Blueprint

Trillions of PDF files are generated every year, each file likely consisting of multiple pages filled with various content types, including text, images,... 8 MIN READ
Decorative image of images and text being fed into a computer and resulting in a synthesized output.
Generative AI Mar 20, 2024

An Easy Introduction to Multimodal Retrieval-Augmented Generation

A retrieval-augmented generation (RAG) application has exponentially higher utility if it can work with a wide variety of data types—tables, graphs, charts,... 12 MIN READ