Navya Battula

Navya Battula

Highly motivated and skilled AI Engineer with over an year of experience in developing and deploying Generative AI and Deep learning models. Demonstrated expertise in using cutting-edge AI tools and frameworks to build robust, scalable solutions. Proficient in a wide range of technologies including PySpark, Hadoop, PostgreSQL, TensorFlow, PyTorch, Open CV and more. Proven track record of success through hands-on projects and internships, with significant contributions in Computer Vision, Machine Learning, ChatBots and AI-driven document management systems. Published researcher with a strong foundation in both theoretical and applied AI.

Blog posts

2025

Prompt Engineering vs RAG vs Finetuning: Strategic AI Customization guide

10 minute read

Published:

In today’s rapidly evolving AI landscape, off-the-shelf large language models (LLMs) often fall short when faced with specialized business requirements. While these foundation models possess remarkable general capabilities, they frequently struggle with domain-specific terminology, proprietary data contexts, and unique organizational needs. This performance gap has catalyzed three powerful customization approaches: Prompt Engineering, Retrieval-Augmented Generation (RAG), and Fine-Tuning. Each method offers distinct advantages for transforming generic AI into a precision instrument for specialized tasks.

Decoding MCP: A comparison between Model Context Protocol vs Rest API

4 minute read

Published:

The AI Isolation Problem: Why MCP Was Born

Picture a brilliant consultant locked in a windowless room. No internet, no documents, no tools—just raw intelligence. This was the reality of AI systems before MCP. Despite their astonishing capabilities, large language models (LLMs) remained trapped in silos, disconnected from the databases, APIs, and tools that could make them truly useful. Every new integration—whether fetching live data from PostgreSQL or automating Blender 3D modeling—required custom code, special prompting, and fragile plumbing. Developers faced an N×M integration nightmare: N AI models needing bespoke connections to M data sources.

Beyond ChatGPT: How Block Diffusion Bridges the Gap in Language Modeling

3 minute read

Published:

As a researcher who’s spent years wrestling with language model limitations, I still remember my frustration when ChatGPT choked on generating coherent text for my queries. That fundamental tension—between the creativity of diffusion models and the precision of autoregressive architectures—has haunted our field. Until now. The breakthrough work in “Block Diffusion” (Arriola et al., ICLR 2025) isn’t just another incremental improvement—it’s the architectural bridge we’ve desperately needed. Let me walk you through why this paper could be an interesting direction for the future of Language Modeling.

Collective Transport: Engineering without Blue Print

7 minute read

Published:

Ant colonies routinely achieve the remarkable feat of transporting objects far exceeding individual capacity—from hefty food items to nesting materials—often navigating complex and cluttered terrains. This stands in stark contrast to coordinated human efforts, which often rely on explicit planning and communication and can falter under similar constraints. The ants’ success hinges not on a pre-designed plan, but on sophisticated, decentralized strategies emerging from local interactions.

The Poor Man’s Finetuning Duel: A comprehensive report on LLM fine tuning on Llama and DeepSeek

7 minute read

Published:

Drawing on extensive (and often frustrating) experience with CUDA memory limitations, I approached the prevalent claims of “efficient” 7B model fine-tuning with skepticism. Benchmarking Llama-2-7B and DeepSeek-7B under strictly controlled conditions (single A100, 40GB VRAM) yielded results that were profoundly surprising. They exposed a significant gap between the rhetoric of efficiency and its practical reality in constrained environments. This analysis fundamentally altered my perspective; allow me to detail this critical reality check.

2024

Unlocking the Magic of ChatGPT: A Journey Through Transformer-Based Language Models

4 minute read

Published:

Imagine an AI that crafts poetry, debugs code, and explains quantum physics—all while adapting to your unique conversational style. This isn’t science fiction; it’s the reality of ChatGPT, a pinnacle achievement in modern AI. But how does it really work? The secret lies in a revolutionary architecture called the Transformer. In this deep dive, we’ll demystify Transformers, build a Shakespeare-generating model from scratch, and reveal what powers ChatGPT’s linguistic brilliance.