Documentation is the first thing to go stale in any codebase. Code changes every week, and the docs rarely keep up. OpenWiki, an open source CLI from LangChain, tackles this by writing documentation for your repo and keeping it updated as the code changes. It is built mainly for coding …

Shipping an LLM app is the easy part. Knowing why it gave a wrong answer at 2 a.m. on a Tuesday is the hard part. Traditional software fails loudly with a stack trace. LLM apps fail quietly: the code runs, the API returns 200, and the answer is confidently …

Working with Language Models

Understanding How Language Models Work in LangChain

Large Language Models (LLMs) are the engines behind modern AI applications. Whether you're building a chatbot, a document assistant, or an AI-powered workflow, you'll spend most of your time interacting with language models.

But not all models work the …

Master LangChain's output parsers—from simple list extraction to self-healing systems that automatically fix parsing errors. Learn when to use each parser and how to build robust AI applications.

Learn why structured outputs matter and how to build reliable validation pipelines in LangChain with typed outputs, JSON schemas, and error handling strategies.

Learn how to safely manage prompt changes in production LangChain applications. Discover strategies for testing, versioning, A/B testing, and monitoring prompt performance.

Learn how to build personalized, context-aware prompts in LangChain that adapt to individual users, their preferences, history, and behavior for better AI experiences.

Introduction

In previous posts, we explored prompt anatomy and hygiene. Now it's time to put that knowledge into practice by building reusable prompt templates that can adapt to different scenarios without code duplication.

Think of prompt templates as blueprints—you design them once, then fill in the details dynamically based …

Learning Through Examples

Few-shot learning is one of the most powerful techniques in prompt engineering. Instead of just telling the model what to do, you show it examples of the desired behavior. This dramatically improves output quality and consistency.

Few-Shot Learning Basics

The concept is simple: provide the model with …

Understanding Prompt Structure

When working with LangChain, understanding how to structure prompts is fundamental to getting consistent, high-quality outputs from language models. A well-structured prompt is the difference between vague responses and precise, actionable results.

Anatomy of a Prompt

Every effective prompt has three core components that work together:

1 …