All Blogs
Autoregressive Models
In this blog, we will learn about Autoregressive Models, the family of models that generate one piece at a time by predicting the next step from the past.
Large Reasoning Models (LRMs)
In this blog, we will learn about Large Reasoning Models (LRMs), how they are different from standard Large Language Models, how they think before they answer, how they are trained, and when we must use them.
Continuous Batching in LLMs
In this blog, we will learn about Continuous Batching, a technique that lets LLM servers handle many more users at the same time by keeping the GPU busy at every single step of generation.
Small Language Models (SLMs)
In this blog, we will learn about Small Language Models (SLMs), what counts as small, why they matter, where they shine, and the trade-offs we must keep in mind.
Multimodal AI
In this blog, we will learn about Multimodal AI, what it means, why it matters, how it works, and where we use it in the real world.
LLM Routing
In this blog, we will learn about LLM Routing, why it matters, and how to send each user query to the right LLM based on cost, latency, and quality.
Context Engineering
In this blog, we will learn about Context Engineering - what it is, why it has become the most important skill for building reliable AI applications, how it differs from Prompt Engineering, the components that make up the context, common patterns like RAG, few-shot examples, tools, and memory, and the best practices and common mistakes to keep in mind.
Reflection Agent
In this blog, we will learn about the Reflection Agent - what it is, how it is built, its anatomy, how it generates, critiques, and revises its own work, and how to handle its common failure modes.
Speculative Decoding
In this blog, we will learn about Speculative Decoding - what it is, why LLM generation is slow without it, how a small draft model and a big target model work together to produce tokens faster, the rejection sampling math that guarantees no quality loss, real numbers showing the 2x to 3x speedup, where it is used in production, and the trade-offs to watch out for.