Navya Battula
Highly motivated and skilled ML Engineer with over 3 years 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 Gen AI technologies and Frameworks like Pytorch, Langchain, Agentic AI, etc. Proven track record of buidling state of the art applications in the domain of AI assistants, model finetuning, Document management systems, Recommendation Systems etc. Published researcher with a strong foundation in both theoretical and applied AI.
- Pune, India
- Acuitilabs
- Google Scholar
- Github
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Prompt Engineering vs RAG vs Finetuning: Strategic AI Customization guide
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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
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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.