If you have built a RAG (Retrieval-Augmented Generation) system, you have likely faced the “Lost in the Middle” problem. You ask your AI a question. It retrieves 10 documents. But the one document that actually contains the answer is buried at position #7. The LLM gets distracted by the noise in documents #1–6 and gives […]
The Accuracy Booster: Demystifying Cross-Encoder Reranking Read More »
In our last post, we discussed the API Gateway as the “Head Waiter” of your software architecture. But how exactly do you place your order with that waiter? For a long time, there was really only one standard way to talk to a server: REST. It was the industry standard—reliable, predictable, and rigid. But as
REST vs. GraphQL: The Battle for Data Efficiency Read More »
Beyond the Magic: Deconstructing the Mechanics of Large Language Models To the casual observer, interacting with a Large Language Model (LLM) feels uncannily like conversing with a sentient being. However, beneath the veneer of wit and fluency lies a mathematical reality: these systems are not thinking entities, but rather highly sophisticated engines of probability. They
Understanding LLM & How it works Read More »
Every app you use whether it’s a bank account or a TikTok feed—relies on a database. It is the digital vault where all information lives. But not all vaults are built the same. For decades, there was only one way to store data: strictly and neatly. Then, the internet exploded with messy data (tweets, likes,
The Battle of the Databases: SQL vs. NoSQL Read More »
In our previous post, we talked about Composable Commerce—the idea of building your software stack like a custom LEGO set rather than buying a pre-glued statue. But how do you know if a piece of software is actually “LEGO-compatible”? Enter MACH. MACH is not a specific software; it is a philosophy. It is a set
The Blueprint of the Future: Demystifying MACH Architecture Read More »
Vector search is the technology powering the most intelligent applications we use daily—from product recommendations and image lookups to smart chatbots. But what happens when you need to search millions, or even billions, of these data points instantly?
The answer lies in two powerful techniques: Approximate Nearest Neighbor (ANN) and the revolutionary HNSW (Hierarchical Navigable Small-World Graph) algorithm.
ANN is a fast shortcut method that finds the “closest matches” to your query based on meaning (vectors), not just exact keywords. HNSW takes this a step further, acting like a multi-level navigation system in a massive data supermarket.
Instead of scanning every item, HNSW quickly travels from the top-level categories (sports, electronics) down to the exact shelf (running shoes) in milliseconds.
Dive into the full post to understand exactly how ANN and HNSW make lightning-fast, highly accurate AI search possible.
Vector search – ANN & HNSW Read More »
Introduction: The “Roadblock” Problem In vector search, we often talk about speed and accuracy. But there is a major flaw in the most popular algorithm (HNSW) that most people don’t realize until they go into production. HNSW is great at finding “Similar items.” But it is terrible at finding “Similar items… that also match a
Why Filtering Breaks HNSW — and How ACORN Fixes It Read More »
When you log into Salesforce, Slack, or Gmail, you aren’t just opening a program; you are entering a massive, invisible city. In the old days, software was like a custom-built house. You bought the CD-ROM, installed it on your server, and you owned that specific instance. If it broke, you had to fix it. Modern
The Invisible Skeleton: Core SaaS Architecture Explained Read More »
If you have used ChatGPT, Claude, or any modern AI tool, you have interacted with a Transformer. It is the specific architecture that turned Artificial Intelligence from a stumbling toddler into a fluent, reasoning assistant. But what exactly is a Transformer? And what is this “Attention” mechanism everyone keeps talking about? Today, we are going
The Engine Behind the AI Revolution: Demystifying Transformers & Attention Read More »
When you type a prompt into ChatGPT, you might think the AI is reading your words just like a human would. It isn’t. Computers cannot inherently understand language; they only understand math. Before an AI can process your request, your text must be chopped up, converted into numbers, and fed into the neural network. This
From Text to Numbers: Understanding Tokenization & BPE Read More »
