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

# Create Custom LLM Prompts

> Learn about different use cases for LeMUR with these examples.

<Tip>
  If you want a Quickstart, see [Apply LLMs to audio files](/docs/getting-started/apply-llms-to-audio-files).
</Tip>

<Info>
  Before you start

  To use LeMUR, you need an [AssemblyAI account](https://www.assemblyai.com/dashboard/signup) with a credit card set up.
</Info>

## Custom prompt example

If you want to send a custom prompt to the LLM, you can use the [LeMUR Task](https://assemblyai.com/docs/api-reference/lemur/task) and apply the model to your transcribed audio files.

<CodeGroup>
  ```python Python theme={"system"}
  import assemblyai as aai

  aai.settings.api_key = "YOUR_API_KEY"

  # Step 1: Transcribe an audio file.
  # audio_file = "./local_file.mp4"
  audio_file = "https://assembly.ai/call.mp4"

  transcriber = aai.Transcriber()
  transcript = transcriber.transcribe(audio_file)

  # Step 2: Define your prompt.
  prompt = "What was the emotional sentiment of the phone call?"

  # Step 3: Apply LeMUR.
  result = transcript.lemur.task(
      prompt, final_model=aai.LemurModel.claude3_5_sonnet
  )

  print(result.response)
  ```

  ```typescript Typescript theme={"system"}
  import { AssemblyAI } from 'assemblyai'

  const client = new AssemblyAI({
    apiKey: 'YOUR_API_KEY'
  })

  const run = async () => {
    // Step 1: Transcribe an audio file.
    // const audioFile = './local_file.mp4'
    const audioFile = 'https://assembly.ai/call.mp4'
    const transcript = await client.transcripts.transcribe({ audio: audioFile })

    // Step 2: Define your prompt.
    const prompt = 'What was the emotional sentiment of the phone call?'

    // Step 3: Apply LeMUR.
    const { response } = await client.lemur.task({
      transcript_ids: [transcript.id],
      prompt,
      final_model: 'anthropic/claude-3-5-sonnet'
    })

    console.log(response)
  }

  run()
  ```

  ```go Go theme={"system"}
  package main

  import (
      "context"
      "fmt"

      aai "github.com/AssemblyAI/assemblyai-go-sdk"
  )

  func main() {
      ctx := context.Background()

      client := aai.NewClient("YOUR_API_KEY")

      // Step 1: Transcribe an audio file. For local files see our Getting Started guides.
      audioURL := "https://assembly.ai/call.mp4"
      transcript, _ := client.Transcripts.TranscribeFromURL(ctx, audioURL, nil)

      // Step 2: Define your prompt.
      prompt := "What was the emotional sentiment of the phone call?"

      // Step 3: Apply LeMUR.
      var params aai.LeMURTaskParams
      params.Prompt = aai.String(prompt)
      params.TranscriptIDs = []string{aai.ToString(transcript.ID)}
      params.FinalModel = "anthropic/claude-3-5-sonnet"

      result, _ := client.LeMUR.Task(ctx, params)

      fmt.Println(*result.Response)
  }
  ```

  ```java Java theme={"system"}
  import com.assemblyai.api.AssemblyAI;
  import com.assemblyai.api.resources.transcripts.types.*;
  import com.assemblyai.api.resources.lemur.requests.*;
  import java.util.List;

  public final class App {
      public static void main(String[] args) {

          AssemblyAI client = AssemblyAI.builder()
                  .apiKey("YOUR_API_KEY")
                  .build();

          // Step 1: Transcribe an audio file. For local files see our Getting Started guides.
          String audioUrl = "https://assembly.ai/call.mp4";
          Transcript transcript = client.transcripts().transcribe(audioUrl);

          // Step 2: Define your prompt.
          String prompt = "What was the emotional sentiment of the phone call?";

          // Step 3: Apply LeMUR.
          var params = LemurTaskParams.builder()
                  .prompt(prompt)
                  .transcriptIds(List.of(transcript.getId()))
                  .finalModel(LemurModel.ANTHROPIC_CLAUDE3_5_SONNET)
                  .build();

          var response = client.lemur().task(params);

          System.out.println(response.getResponse());
      }
  }
  ```

  ```csharp C# theme={"system"}
  using AssemblyAI;
  using AssemblyAI.Lemur;
  using AssemblyAI.Transcripts;

  var client = new AssemblyAIClient("YOUR_API_KEY");

  // Step 1: Transcribe an audio file. For local files see our Getting Started guides.
  var transcript = await client.Transcripts.TranscribeAsync(new TranscriptParams
  {
      AudioUrl = "https://assembly.ai/call.mp4"
  });

  // Step 2: Define your prompt.
  const string prompt = "What was the emotional sentiment of the phone call?";

  // Step 3: Apply LeMUR.
  var lemurTaskParams = new LemurTaskParams
  {
      Prompt = prompt,
      TranscriptIds = [transcript.Id],
      FinalModel = LemurModel.AnthropicClaude3_5_Sonnet
  };

  var response = await client.Lemur.TaskAsync(lemurTaskParams);

  Console.WriteLine(response.Response);
  ```

  ```ruby Ruby theme={"system"}
  require 'assemblyai'

  client = AssemblyAI::Client.new(api_key: 'YOUR_API_KEY')

  # Step 1: Transcribe audio file. For local files see our Getting Started guides.
  audio_url = 'https://assembly.ai/call.mp4'
  transcript = client.transcripts.transcribe(audio_url: audio_url)

  # Step 2: Define your prompt.
  prompt = "What was the emotional sentiment of the phone call?"

  # Step 3: Apply LeMUR.
  response = client.lemur.task(
    prompt: prompt,
    transcript_ids: [transcript.id],
    final_model: AssemblyAI::Lemur::LemurModel::ANTHROPIC_CLAUDE3_5_SONNET
  )

  puts response.response
  ```
</CodeGroup>

## Ideas to get you started

| Use case                         | Example prompt                                                                               |
| -------------------------------- | -------------------------------------------------------------------------------------------- |
| Question & Answer                | *"Identify any patterns or trends based on the transcript"*                                  |
| Quote or Citation                | *"List the timestamp X topic was discussed, provide specific citations"*                     |
| Closed-ended questions           | *"Did the customer express a positive sentiment in the phone call?"*                         |
| Sentiment analysis               | *"What was the emotional sentiment of the phone call?"*                                      |
| Summaries                        | *"Summarize key decisions and important points from the phone call transcript"*              |
| Summarize audio segments         | *"Summarize the key events of each chapter"*                                                 |
| Generate titles and descriptions | *"Generate an attention-grabbing YouTube title based on the video transcript"*               |
| Generate tags                    | *"Generate keywords that can be used to describe the key themes of the conversation"*        |
| Action items                     | *"What action items were assigned to each participant?"*                                     |
| Generate content                 | *"Generate a blog post with key information presented in bullet points from the transcript"* |
| Paraphrasing                     | *"Rephrase X segment from the transcript in a different way"*                                |

You can find more ideas and code examples in the [AssemblyAI Cookbook repo](https://github.com/AssemblyAI/cookbook) on Github.