> ## Documentation Index
> Fetch the complete documentation index at: https://together-ai-preview.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Use these examples to learn best practices for using the inference API.

## [Prompt formatting](#prompt-formatting)

The following code gives an example of automatically formatting a prompt during inference to match the expected format for the given model. Note, not all models include a <button aria-label="Copy Code" class="rdmd-code-copy fa" /><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">prompt\_format</code>; make sure you validate that a prompt format is present in your code.

```python python theme={null}
import together

together.api_key = "xxxxx"

model = "garage-bAInd/Platypus2-70B-instruct"

stop_words = list(together.Models.info(model)['config']['stop']) # =
['</s>', '###']
prompt_format = str(together.Models.info(model)['config']['prompt_format'])
# = '### Instruction:\n{prompt}\n### Response:\n'

prompt = "hello"
formatted_prompt = prompt_format.format(prompt=prompt)

for token in together.Complete.create_streaming(prompt=formatted_prompt,
model='upstage/SOLAR-0-70b-16bit', stop=stop_words):
    print(token, end="", flush=True)
print("\n")
```

## [Grammar correction](#grammar-correction)

Try RedPajama-INCITE-Chat-3B to correct sentences to standard English.

1. For example, begin the prompt by asking to correct the grammar of a sentence:

<Tabs>
  <Tab title="Prompt 1">
    Correct this to standard English:
    I no Sandwich want.
  </Tab>

  <Tab title="Prompt 2">
    Correct this to standard English:
    If I’m stressed out about something, I tend to have problem to fall asleep.
  </Tab>
</Tabs>

2. Sample response

<Tabs>
  <Tab title="Response 1">
    I don't want a sandwich.
  </Tab>

  <Tab title="Response 2">
    Correct this to standard English:
    If I'm stressed out about something, I tend to have a hard time falling asleep
  </Tab>
</Tabs>

3. Send the prompt to the API with any appropriate parameters. The code below shows an example in Python using the `requests` package.

```python python theme={null}
import requests

url = "https://api.together.xyz/inference"

payload = {
    "model": "togethercomputer/togethercomputer/RedPajama-INCITE-7B-Chat",
    "prompt": "Correct this to standard English:\nI no Sandwich want.",
    "max_tokens": 256,
    "stop": ".",
    "temperature": 0.1,
    "top_p": 0.7,
    "top_k": 50,
    "repetition_penalty": 1
}
headers = {
    "accept": "application/json",
    "content-type": "application/json",
    "Authorization": "Bearer <YOUR_API_KEY>",
  	"User-Agent": "<YOUR_APP_NAME>",
}    

response = requests.post(url, json=payload, headers=headers)

print(response.text)

```

## [Further reading](#further-reading)

Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. The [Prompt Engineering Guide](https://www.promptingguide.ai) is a great introduction to the subject.

Updated 22 days ago

***