Prompts

Store, version, and organize prompts for LLM applications

This feature is only accessible through the Python SDK. All code examples on this page are provided in Python.

Creating, evaluating, and refining prompts is a core activity for AI engineers. Small changes to a prompt can have big impacts on your application’s behavior. Weave lets you create prompts, save and retrieve them, and evolve them over time.

Weave is unopinionated about how a Prompt is constructed. If your needs are simple you can use our built-in weave.StringPrompt or weave.MessagesPrompt classes. If your needs are more complex you can subclass those or our base class weave.Prompt and override the format method.

When you publish one of these objects with weave.publish, it will appear in your Weave project on the “Prompts” page.

StringPrompt

import weave
weave.init('intro-example')

# highlight-next-line
system_prompt = weave.StringPrompt("You are a pirate")
# highlight-next-line
weave.publish(system_prompt, name="pirate_prompt")

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {
      "role": "system",
      # highlight-next-line
      "content": system_prompt.format()
    },
    {
      "role": "user",
      "content": "Explain general relativity in one paragraph."
    }
  ],
)

Perhaps this prompt does not yield the desired effect, so we modify the prompt to be more clearly instructive.

import weave
weave.init('intro-example')

# highlight-next-line
system_prompt = weave.StringPrompt("Talk like a pirate. I need to know I'm listening to a pirate.")
weave.publish(system_prompt, name="pirate_prompt")

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {
      "role": "system",
      # highlight-next-line
      "content": system_prompt.format()
    },
    {
      "role": "user",
      "content": "Explain general relativity in one paragraph."
    }
  ],
)

When viewing this prompt object, I can see that it has two versions.

I can also select them for comparison to see exactly what changed.

MessagesPrompt

The MessagesPrompt can be used to replace an array of Message objects.

import weave
weave.init('intro-example')

# highlight-next-line
prompt = weave.MessagesPrompt([
    {
        "role": "system",
        "content": "You are a stegosaurus, but don't be too obvious about it."
    },
    {
        "role": "user",
        "content": "What's good to eat around here?"
    }
])
weave.publish(prompt, name="dino_prompt")

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4o",
  # highlight-next-line
  messages=prompt.format(),
)

Parameterizing prompts

As the format method’s name suggests, you can pass arguments to fill in template placeholders in the content string.

import weave
weave.init('intro-example')

# highlight-next-line
prompt = weave.StringPrompt("Solve the equation {equation}")
weave.publish(prompt, name="calculator_prompt")

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {
      "role": "user",
      # highlight-next-line
      "content": prompt.format(equation="1 + 1 = ?")
    }
  ],
)

This also works with MessagesPrompt.

import weave
weave.init('intro-example')

# highlight-next-line
prompt = weave.MessagesPrompt([
{
    "role": "system",
    "content": "You will be provided with a description of a scene and your task is to provide a single word that best describes an associated emotion."
},
{
    "role": "user",
    "content": "{scene}"
}
])
weave.publish(prompt, name="emotion_prompt")

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4o",
  # highlight-next-line
  messages=prompt.format(scene="A dog is lying on a dock next to a fisherman."),
)