Building a ‘Human-in-the-Loop’ Approval Gate for Autonomous Agents
In agentic AI systems , when an agent’s execution pipeline is intentionally halted, we have what is known as a state-managed interruption .
From Prompt to Prediction: Understanding Prefill, Decode, and the KV Cache in LLMs
This article is divided into three parts; they are: • How Attention Works During Prefill • The Decode Phase of LLM Inference • KV Cache: How to Make Decode More Efficient Consider the prompt: Today’s weather is so .
From Prompt to Prediction: Understanding Prefill, Decode, and the KV Cache in LLMs
This article is divided into three parts; they are: • How Attention Works During Prefill • The Decode Phase of LLM Inference • KV Cache: How to Make Decode More Efficient Consider the prompt: Today’s weather is so .
7 Essential Python Itertools for Feature Engineering
Feature engineering is where most of the real work in machine learning happens.
7 Essential Python Itertools for Feature Engineering
Feature engineering is where most of the real work in machine learning happens.
LlamaAgents Builder: From Prompt to Deployed AI Agent in Minutes
Creating an AI agent for tasks like analyzing and processing documents autonomously used to require hours of near-endless configuration, code orchestration, and deployment battles.
Vector Databases Explained in 3 Levels of Difficulty
Traditional databases answer a well-defined question: does the record matching these criteria exist?
5 Practical Techniques to Detect and Mitigate LLM Hallucinations Beyond Prompt Engineering
My friend who is a developer once asked an LLM to generate documentation for a payment API.
Beyond the Vector Store: Building the Full Data Layer for AI Applications
If you look at the architecture diagram of almost any AI startup today, you will see a large language model (LLM) connected to a vector store.
7 Steps to Mastering Memory in Agentic AI Systems
Memory is one of the most overlooked parts of agentic system design.