GeminiAI Prompt Chaining

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Configuring and publishing the process

Open the

Prompt Chaining Gemini

process.

On the

Temp Fields

tab of the Start step, the default

Model_LLM

gemini-1.5 pro

. You can optionally edit the model version. For information about changing the model version, see the Gemini documentation.

In the

Create Prompt

step, enter the prompt instructions in the

Assignments

field by updating the

Prompt_Configuration

and

Prompt_Request

fields using the Expression Editor, as shown in the following sample code:

For Prompt_Configuration: 
 <generationConfig>
      <stopSequences>.</stopSequences>
      <candidateCount>1</candidateCount>
      <maxOutputTokens>200</maxOutputTokens>
      <temperature>0.5</temperature>
      <topP>0.5</topP>
      <topK>2</topK>
   </generationConfig>

For Prompt_Request:
<Generate_Content_Request>
   <contents>
      <parts>
         <text>Your behaviour is {$input.First_System_Prompt}, Topic is {$input.First_User_Prompt}</text>
      </parts>
      <role>user</role>
   </contents>
 <generationConfig>
      <stopSequences>{$temp.Prompt_Configuration[1]/stopSequences}</stopSequences>
      <candidateCount>{$temp.Prompt_Configuration[1]/candidateCount }</candidateCount>
      <maxOutputTokens>{$temp.Prompt_Configuration[1]/maxOutputTokens }</maxOutputTokens>
      <temperature>{$temp.Prompt_Configuration[1]/temperature }</temperature>
      <topP>{$temp.Prompt_Configuration[1]/topP }</topP>
      <topK>{$temp.Prompt_Configuration[1]/topK }</topK>
   </generationConfig>
</Generate_Content_Request>

For the

Prompt_Configuration

field, enter values for the following properties:

Property	Description
stopSequences	Contains sequences of characters or strings that stop the model's output. It controls where the model must end its response.
candidateCount	Specifies the number of response candidates that the model must generate. For example, if the value is set to 1, the model generates one response. If set to a higher number, the model generates that many alternative responses for the same input.
maxOutputTokens	Defines the maximum number of tokens the model can generate in its response. Setting a limit ensures that the response is concise and fits within the desired length constraints.
temperature	Controls the randomness of the model's output. A lower value makes the output more deterministic, while a higher value increases randomness and creativity. For example, a temperature of 0.5 balances between deterministic and creative outputs.
topP	Determines the cumulative probability threshold for token selection. The model considers the smallest set of tokens whose cumulative probability meets or exceeds topP. For example, if topP is set to 0.1, the model considers only the top 10% most probable tokens at each step.
topK	Limits the number of the highest-probability tokens to consider during response generation. For example, if topK is set to 2, the model considers only the top 2 tokens at each step, controlling output diversity and quality.

After configuring the prompt instructions, the process send the details to the LLM to fetch the required response, and then stores the first response.

In the

Create Second Prompt

step, in the

Assignments

field, update the

Prompt_Request

field using the Expression Editor as shown in the following sample code:

 
<Generate_Content_Request>
<contents>
      <parts>
         <text>Your behaviour is : {$input.First_System_Prompt}, Topic is : {$input.First_User_Prompt}</text>
      </parts>
      <role>user</role>
   </contents>
 <contents>
      <parts>
         <text>{$temp.Prompt_Response}</text>
      </parts>
      <role>model</role>
   </contents>
      <contents>
      <parts>
         <text>Your additional instructions are : {$input.Second_System_Prompt}. Question is {$input.Second_User_Prompt}</text>
      </parts>
      <role>user</role>
   </contents>
   <generationConfig>
      <stopSequences>{$temp.Prompt_Configuration[1]/stopSequences}</stopSequences>
      <candidateCount>{$temp.Prompt_Configuration[1]/candidateCount }</candidateCount>
      <maxOutputTokens>{$temp.Prompt_Configuration[1]/maxOutputTokens }</maxOutputTokens>
      <temperature>{$temp.Prompt_Configuration[1]/temperature }</temperature>
      <topP>{$temp.Prompt_Configuration[1]/topP }</topP>
      <topK>{$temp.Prompt_Configuration[1]/topK }</topK>
   </generationConfig>
</Generate_Content_Request>

The LLM uses both the requests as an instruction to prepare the final response.

Save and publish the process.

Using the GeminiAI Prompt Chaining recipe

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