Thuwarakesh Murallie

These 3 starter projects only take a weekend (and a few cups of coffee, maybe) Thuwarakesh Murallie · Follow Published in Towards Data Science · 13 min read · 7 hours ago — Photo by Photo By: Kaboompics.com from Pexels. Talk of the town: AI agents will replace SAAS. This wasn’t a random statement made by any drug dealer. It was Satya Nadella, the CEO of Microsoft, saying this. If you’re a developer or anyone

Can multimodal LLMs infer basic charts accurately? Thuwarakesh Murallie · Follow Published in Towards Data Science · 31 min read · 7 hours ago — Image created by the author using Flux 1.1 [Pro] Multimodal LLMs (MLLMs) promise that they can interpret anything on an image. It’s true for most cases, such as image captioning and object detection. But can it reasonably and accurately understand data presented on a chart? If you really want to

Do you need more GPUs or a modern GPU? How do you make infrastructure decisions? Thuwarakesh Murallie · Follow Published in Towards Data Science · 6 min read · 12 hours ago — Image created by the author using Dalle-E-2024 How does it feel when a group of users suddenly start using an app that only you and your dev team have used before? That’s the million-dollar question of moving from prototype to production. As

Here’s how not to waste your budget on evaluating models and systems Thuwarakesh Murallie · Follow Published in Towards Data Science · 14 min read · 9 hours ago — mage created by the author using Flux1.1 Pro. You can build a fortress in two ways: Start stacking bricks one above the other, or draw a picture of the fortress you’re about to build and plan its execution; then, keep evaluating it against your plan.

Here are my favorite techniques — one is faster, the other is more accurate. Thuwarakesh Murallie · Follow Published in Towards Data Science · 9 min read · 9 hours ago — Photo by Alexander Grey I build RAG apps; it’s fun! But the apps I build don’t do well in production. They’re promising prototypes, but they never go live! The culprit is almost always the retrieval. Come on, this is the heart of RAGs.

The costly yet powerful splitting technique for superior RAG retrieval Thuwarakesh Murallie · Follow Published in Towards Data Science · 8 min read · 6 hours ago — Photo by Nataliya Vaitkevich Good chunks make good RAGs. Chunking, embedding, and indexing are critical aspects of RAGs. A RAG app that uses the appropriate chunking technique performs well in terms of output quality and speed. When engineering an LLM pipeline, we use different strategies to split

How to implement semantic chunking and gain better results. Thuwarakesh Murallie · Follow Published in Towards Data Science · 10 min read · 6 hours ago — Photo by vackground.com on Unsplash Neighbors could still be different. Language models come with a context limit. For newer OpenAI models, this is around 128k tokens, roughly 80k English words. This may sound big enough for most use cases. Still, large production-grade applications often need to refer to

An LLM can handle general routing. Semantic search can handle private data better. Which one would you pick? Thuwarakesh Murallie · Follow Published in Towards Data Science · 10 min read · 11 hours ago — Photo by Monstera Production A single prompt cannot handle everything, and a single data source may not be suitable for all the data. Here’s something you often see in production but not in demos: You need more than one