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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Basic prompting
Prompts are the inputs given to a large language model. They are the interface between human instruction and computing tasks.
By submitting natural language requests to an LLM in a prompt, you can answer questions, generate text, and solve problems.
This article will show you how to use Langflow's prompt tools to submit basic prompts to an LLM, and how different prompting strategies can change your results.
## Prerequisites
1. Install Langflow.
```bash
pip install langflow
```
2. Start a local Langflow instance.
```bash
langflow
```
Result:
```
│ Welcome to ⛓ Langflow │
│ │
│ Access http://127.0.0.1:7860 │
│ Collaborate, and contribute at our GitHub Repo 🚀 │
```
Alternatively, visit us on [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment.
## Create components
For this example, you'll build a OpenAI chat flow with four components, and then extend it with prompt templates to see the results.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/basic-prompting.png",
dark: "img/basic-prompting.png",
}}
style={{ width: "80%", margin: "20px auto" }}
/>
1. Create a **ChatOpenAI** component.
2. In the OpenAI API Key field, paste your OpenAI API Key (`sk-...`).
3. Create an **LLMChain** component. Connect the LLM input to the ChatOpenAI LLM's output.
4. Create a **ChatPromptTemplate** component. Connect the output to the LLMChain Prompt's input.
5. Create a **SystemMessagePromptTemplate** component. This represents a system message, which tells the model how to behave. The Prompt field can stay as default. Connect it to the input of **ChatPromptTemplate**.
6. Create a **HumanMessagePromptTemplate** component. This represents a message from the user. In the Prompt field, enter `{text}`. Connect it to the input of **ChatPromptTemplate**.
7. Select the Run icon. LangFlow will check your components for errors and return "Flow is Ready to Run".
8. Select the Messages icon. A chat window will open to run your prompt.
Chat with the bot to see how it responds according to the behavior described in Prompt.
9. Change the behavior in the Prompt field of **SystemMessagePromptTemplate** and see what happens - for example, suggest it be an unhelpful, grumpy assistant, and see how the results change.
## Other prompts
Langflow also has **PromptTemplate** and **ChatMessagePromptTemplate** components.
Connect **PromptTemplate** to the **LLMChain** Prompt output for use as a one-shot prompt.
**ChatMessagePromptTemplate** has a `role` field that can be defined as `system`, `user`, `function`, or `assistant`, replacing the more specific template components you used in the example.