Structured Outputs vs. Function Calling: Which Should Your Agent Use?
Language models (LMs), at their core, are text-in and text-out systems.
How to Implement Tool Calling with Gemma 4 and Python
The open-weights model ecosystem shifted recently with the release of the
Beyond Vector Search: Building a Deterministic 3-Tiered Graph-RAG System
The Roadmap to Mastering Agentic AI Design Patterns
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A Hands-On Guide to Testing Agents with RAGAs and G-Eval
Handling Race Conditions in Multi-Agent Orchestration
If you’ve ever watched two agents confidently write to the same resource at the same time and produce something that makes zero sense, you already know what a race condition feels like in practice.
Top 5 Reranking Models to Improve RAG Results
If you have worked with retrieval-augmented generation (RAG) systems, you have probably seen this problem.
7 Machine Learning Trends to Watch in 2026
A couple of years ago, most machine learning systems sat quietly behind dashboards.
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 .