Large Language Models on Chen Kai Blog

AI Agents Complete Guide: From Theory to Industrial Practice

Mon, 19 Jan 2026 09:00:00 +0000

A chatbot answers questions. An agent gets things done — it browses, runs code, calls APIs, queries databases, and iterates until the job is complete. The same LLM powers both, but the wrapper differs: an agent runs in a loop with tools, memory, and the ability to inspect its own work.

This guide is the expanded version of that idea. It covers the four core capabilities (planning, memory, tool use, reflection), major framework families, multi-agent collaboration, evaluation, and the production concerns that determine whether an agent succeeds or fails.

Prompt Engineering Complete Guide: From Zero to Advanced Optimization

Tue, 30 Sep 2025 09:00:00 +0000

The same model, two prompts: one achieves 17% accuracy on grade-school math, the other 78%. The difference isn’t magic—it’s prompt engineering. This guide covers the techniques that work, the research behind them, and how to systematically optimize prompts for production.

What You Will Learn#

Foundations — zero-shot, few-shot, many-shot, task decomposition, and the five-block prompt skeleton.
Reasoning techniques — Chain-of-Thought, Self-Consistency, Tree of Thoughts, Graph of Thoughts, ReAct.
Automation — Automatic Prompt Engineering (APE), DSPy, LLMLingua compression.
Practical templates — structured output, code generation, data extraction, multi-turn chat.
Evaluation and debugging — metrics, A/B testing, error analysis, the failure-mode toolkit.

Prerequisites. Basic Python; experience calling any LLM API. No math background required.

LLM Workflows and Application Architecture: Enterprise Implementation Guide

Thu, 31 Jul 2025 09:00:00 +0000

Most LLM tutorials end where the interesting work begins. They show you how to call a chat completion endpoint, attach a vector store, and wrap the whole thing in a Streamlit demo. None of that is wrong, but none of it is what breaks at 3 a.m. when 10,000 users hit your service at once and every other answer is a hallucination.

This article is about everything that comes after the demo. It is opinionated on purpose: production LLM systems are mostly plain distributed systems with one non-deterministic component bolted on, and most of the engineering effort goes into containing that non-determinism. We will work through seven dimensions — application architecture, workflow patterns, the RAG-vs-fine-tune decision, deployment topology, cost, observability, and enterprise integration — keeping each one short, concrete, and grounded in the levers that actually move the needle.