# Prompt Templates and Messages — LangChain / LlamaIndex

Source: https://www.geekswithgeeks.com/en/langchain-llamaindex/c-prompt

> Build reusable prompts with variables.

## Templates with named slots

A **`ChatPromptTemplate`** builds a list of role-tagged **messages** (system, human, AI) from a template with `{variables}`. Invoking it with a dict of values returns a prompt value you can turn into messages. Templates make prompts reusable, versionable and testable, and the template **knows its input variables**, so a missing value fails early with a clear error instead of sending a half-filled prompt. Use `MessagesPlaceholder` for chat history and `FewShotChatMessagePromptTemplate` to inject examples. Keep prompt text in files or constants, not scattered across code.

## Compose with the pipe

Runnables share one interface, so prompts, models and parsers can be piped together.

![Four pieces: prompt, model, parser, pipe.](assets/figures/langchain-llamaindex/section-2-map.svg) — Figure 2.1 — Prompt, model, parser and pipe.

## A chat prompt template, run

I ran this offline in a Python virtual environment with langchain-core 1.6.6, langchain-text-splitters 1.1.2 and llama-index-core 0.14.25. No API key or network call is needed because a fake model or a toy embedding stands in for the real one. The template produces a SystemMessage and a HumanMessage with the variables filled in, and reports that it needs the inputs `question` and `role`.

```python
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a {role}. Answer in one sentence."),
    ("human", "{question}"),
])
value = prompt.invoke({"role": "geography teacher", "question": "What is the capital of India?"})
for m in value.to_messages():
    print(type(m).__name__, "|", m.content)
print(prompt.input_variables)

```

Output:

```
SystemMessage | You are a geography teacher. Answer in one sentence.
HumanMessage | What is the capital of India?
['question', 'role']
```

## Escape literal braces

If your prompt contains JSON braces, write `{{` and `}}` so the template does not treat them as variables.

**Quiz:** What is an advantage of prompt templates?

- [ ] They make the model free
- [x] Reusable prompts that fail early when a variable is missing
- [ ] They hide the prompt from the model
- [ ] They replace parsers

*Answer:* Reusable prompts that fail early when a variable is missing. Templates give structure and validation to prompt building.