In this article, you will learn three concrete techniques for making machine learning model predictions interpretable, covering both global and local explanations across tree-based and neural network architectures.
Author Archive | Shittu Olumide
Designing AI Agents That Can Self-Correct
In this article, you will learn how to design AI agents that can reliably self-correct by grounding their feedback loops in external verification rather than the model’s own judgment.
The End-to-End Agentic AI Pipeline
In this article, you will learn the seven architectural components that separate a production-grade agentic AI system from a demo script, and how each one fits into the agent’s core feedback loop.
An Introduction to Loop Engineering
In this article, you will learn what loop engineering is, where it came from, and how to design autonomous AI agent cycles that run reliably without constant human supervision.
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.
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.
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.
The AI Agent Tech Stack Explained
In this article, you will learn how the seven layers of a production AI agent stack fit together, from the foundation model down to deployment infrastructure.
Agentic Workflow vs. Autonomous Agent: What’s the Difference?
In this article, you will learn how to distinguish agentic workflows from autonomous agents by focusing on who owns control flow — a human writing code in advance, or a model reasoning at runtime.
Building Browser-Using AI Agents in Python
In this article, you will learn how to build AI agents that can browse and interact with real websites using Playwright, browser-use, and LangGraph.