How to Implement Tool Calling with Gemma 4 and Python
The open-weights model ecosystem shifted recently with the release of the
Structured Outputs vs. Function Calling: Which Should Your Agent Use?
Language models (LMs), at their core, are text-in and text-out systems.
Beyond Vector Search: Building a Deterministic 3-Tiered Graph-RAG System
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.
Vector Databases vs. Graph RAG for Agent Memory: When to Use Which
5 Essential Security Patterns for Robust Agentic AI