Skip to main content
Portkey enhances this capability with the raw prompt feature of prompt templates. You can easily add few-shot learning examples to your templates with raw prompt and dynamically update them whenever you want, without needing to modify the prompt templates!

​
How does it work?

Let’s consider a use case where, given a candidate profile and a job description, the LLM is expected to output candidate notes in a specific JSON format.

​
This is how our raw prompt looks:

[
    {
        "role": "system",
        "message": "You output candidate notes in JSON format when given a candidate profile and a job description.",
    },

    {{few_shot_examples}},

    {
        "role": "user",
        "message": "Candidate Profile: {{profile}} \n Job Description: {{jd}}"
    },
]

​
Let’s define our variables:

As you can see, we have added variables few_shot_examples, profile, and jd in the above examples. 
profile = "An experienced data scientist with a PhD in Computer Science and 5 years of experience working with machine learning models in the healthcare industry."

jd = "We are seeking a seasoned data scientist with a strong background in machine learning, ideally with experience in the healthcare sector. The ideal candidate should have a PhD or Master's degree in a relevant field and a minimum of 5 years of industry experience."

​
And now let’s add some examples with the expected JSON structure:

few_shot_examples =

[

 {

  "role": "user",

  "content": "Candidate Profile: Experienced software engineer with a background in developing scalable web applications using Python. Job Description: We’re looking for a Python developer to help us build and scale our web platform.",

 },

 {

  "role": "assistant",

  "content": "{'one-line-intro': 'Experienced Python developer with a track record of building scalable web applications.', 'move-forward': 'Yes', 'priority': 'P1', 'pros': '1. Relevant experience in Python. 2. Has built and scaled web applications. 3. Likely to fit well with the job requirements.', 'cons': 'None apparent from the provided profile.'}",

 },

 {

  "role": "user",

  "content": "Candidate Profile: Recent graduate with a degree in computer science and a focus on data analysis. Job Description: Seeking a seasoned data scientist to analyze large data sets and derive insights."

 },

 {

  "role": "assistant",

  "content": "{'one-line-intro': 'Recent computer science graduate with a focus on data analysis.', 'move-forward': 'Maybe', 'priority': 'P2', 'pros': '1. Has a strong educational background in computer science. 2. Specialized focus on data analysis.', 'cons': '1. Lack of professional experience. 2. Job requires a seasoned data scientist.' }"

  }

]
In this configuration, {{few_shot_examples}} is a placeholder for the few-shot learning examples, which are dynamically provided and can be updated as needed. This allows the LLM to adapt its responses to the provided examples, facilitating versatile and context-aware outputs.

​
Putting it all together in Portkey’s prompt manager:

  1. Go to the “Prompts” page on https://app.portkey.ai/ and Create a new Prompt template with your preferred AI provider.
  2. Selecting Chat mode will enable the Raw Prompt feature:
  1. Click on it and paste the raw prompt code from above. And that’s it! You have your dynamically updatable few shot prompt template ready to deploy.

​
Deploying the Prompt with Portkey

Deploying your prompt template to an API is extremely easy with Portkey. You can use our Prompt Completions API to use the prompt we created.
  • Python
  • NodeJS
  • cURL
from portkey_ai import Portkey

client = Portkey(
    api_key="PORTKEY_API_KEY",  # defaults to os.environ.get("PORTKEY_API_KEY")
)

prompt_completion = client.prompts.completions.create(
    prompt_id="Your Prompt ID", # Add the prompt ID we just created
    variables={
       few_shot_examples: fseObj,
       profile: "",
       jd: ""
    }
)

print(prompt_completion)

# We can also override the hyperparameters
prompt_completion = client.prompts.completions.create(
    prompt_id="Your Prompt ID", # Add the prompt ID we just created
    variables={
       few_shot_examples: fseObj,
       profile: "",
       jd: ""
    }
    max_tokens=250,
    presence_penalty=0.2
)
print(prompt_completion)
You can pass your dynamic few shot learning examples with the few_shot_examples variable, and start using the prompt template in production!

​
Detailed Guide on Few-Shot Prompting

We recommend this guide detailing the research as well as edge cases for few-shot prompting.

​
Support

Facing an issue? Reach out on [email protected] for a quick resolution.
⌘I