> ## Documentation Index
> Fetch the complete documentation index at: https://genai.codewithsiva.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# LCEL Chains

> Connect prompts, models, and output parsers using LangChain Expression Language (LCEL)

Chains are the building blocks of LangChain applications. They allow you to chain together multiple components, such as prompt templates, language models, and output parsers, into a single, unified execution pipeline.

## Why Use LCEL?

LangChain Expression Language (LCEL) is a declarative way to compose chains. It offers several benefits out of the box:

1. **First-class Streaming Support**: When you build your chains with LCEL, you get the best possible time-to-first-token (time elapsed until the first chunk of output comes out).
2. **Async Support**: Any chain built with LCEL can be called both with the synchronous API (e.g., `.invoke()`) and with the asynchronous API (e.g., `.ainvoke()`).
3. **Optimized Parallel Execution**: Whenever your LCEL chains have steps that can be executed in parallel, LangChain executes them automatically.

***

## Structure of a Basic Chain

At its core, a chain consists of:

* **Input**: The starting dictionary or string (e.g., `{"topic": "science"}`).
* **Prompt Template**: Takes raw input and formats it into prompt messages.
* **Model**: Takes prompt messages and returns an AI message response.
* **Parser**: Takes the AI message and extracts raw text or structured data.

Using LCEL, you can link these components together cleanly using the pipe operator (`|`):

```python theme={null}
chain = prompt | model | parser
```

In the next sections, we will explore chains in detail, starting with basic chain configurations to advanced patterns like parallel execution and dynamic routing.