In this article, you will learn what prompt injection and tool misuse are in the context of agentic AI systems, and which defense strategies experts recommend to mitigate them.
Run a Local AI Model with Ollama in 15 Minutes
In this article, you will learn how to get a small language model running locally on your own machine in under 15 minutes using Ollama.
Scikit-Ollama for Scikit-LLM/Ollama Integration
In this article, you will learn how scikit-ollama bridges the scikit-learn interface with locally running Ollama models to perform zero-shot text classification; no cloud API required.
LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does
In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why the LLM-as-a-judge mechanism they all rely on has measurable biases you need to actively design around.
Building AI Agents? Here Are Some Anti-Patterns to Avoid.
In this article, you will learn the architectural and operational anti-patterns that cause AI agent projects to fail, and how to avoid each one.
Choosing the Right AI Agent Memory Strategy: A Decision-Tree Approach
In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple decision tree, one category of information at a time.
LLM Orchestration Frameworks Compared: LangChain vs. LlamaIndex vs. Raw API Calls
In this article, you will learn how LangChain, LlamaIndex, and raw API calls each solve a different layer of the LLM application stack, and how to choose among them based on what your project actually requires.
Tools vs. Subagents: Building Effective AI Agents Without Over-Engineering
In this article, you will learn how to decide whether a given piece of agent functionality should be built as a tool or as a subagent, and how to avoid overengineering your agent architecture in the process.
The Complete Guide to Tool Selection in AI Agents
In this article, you will learn why agent accuracy degrades as a tool catalog grows, and six practical techniques for keeping tool selection accurate and efficient at scale.
Context vs. Memory Engineering in Agentic AI Systems
In this article, you will learn how context engineering and memory engineering solve different problems in agentic AI systems, and how the two disciplines meet at the point where retrieved memory enters the context window.
