In this article, you will learn how to match an LLM inference optimization technique to the actual bottleneck in your workload by working through a six-question decision tree.
Author Archive | Bala Priya C
The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks
In this article, you will learn how agent memory works, what types of memory exist, and how different frameworks approach storage, retrieval, and memory management at scale.
Choosing a Chunking Strategy for Your LLM Apps: A Decision-Tree Approach
In this article, you will learn how to choose the right chunking strategy for a retrieval-augmented generation system by working through a structured decision tree tied to the properties of your documents and retrieval task.
How (and Why) to Build an AI Agent from Scratch in Python
In this article, you will learn what an AI agent is and how to build one from scratch in plain Python using the Anthropic API, without relying on any orchestration framework.
The Roadmap to Mastering LLM Inference Optimization
In this article, you will learn how LLM inference optimization works and which techniques to apply to make language models faster, cheaper, and more reliable in production.
AI Agent Memory Design: What Works and What Doesn’t
In this article, you will learn how to design reliable memory systems for AI agents, covering both the patterns that work and the common architectural mistakes that cause persistent, hard-to-trace failures. Topics we will cover include: What agent memory actually means and how it differs from context, prompts, and static knowledge bases. Write and retrieval […]
Learn Vectorized Thinking in Python Through Examples
In this article, you will learn how to think in terms of vectorized operations using NumPy, replacing slow Python loops with efficient array-level computations.
Retrieval vs. Memory in Agentic AI Systems
In this article, you will learn the conceptual and practical differences between retrieval and memory in agentic AI systems, and how to combine both effectively.
Static vs. Dynamic vs. Continuous Batching in LLM Inference
In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production scale.
Building Agentic Workflows in Python with LangGraph
In this article, you will learn how to build a complete agentic workflow in Python with LangGraph, from a single model call to a tool-using agent with persistent conversation memory.