How do we make the prompt explicit rather than implicit? #61
Replies: 2 comments 1 reply
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Thanks for the feedback! Could you share some code example to explain your concern(what's hidden from developers)? |
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Sure! Take a look 👇 I'm testing a simple coordinator scenario. Here is my agent code: Here's my interaction: 
If you look at the prompt sent to the model, it appears as follows: The prompt is built dynamically, as I understand, with the properties used to construct the sub-agents and our coordinator. For example, while navigating the codebase, I found this class: class _IdentityLlmRequestProcessor(BaseLlmRequestProcessor):
"""Gives the agent identity from the framework."""
@override
async def run_async(
self, invocation_context: InvocationContext, llm_request: LlmRequest
) -> AsyncGenerator[Event, None]:
agent = invocation_context.agent
si = [f'You are an agent. Your internal name is "{agent.name}".']
if agent.description:
si.append(f' The description about you is "{agent.description}"')
llm_request.append_instructions(si)
# Maintain async generator behavior
if False: # Ensures it behaves as a generator
yield # This is a no-op but maintains generator structure
request_processor = _IdentityLlmRequestProcessor()To me as a user, at first, this seems like something magical (and awesome), but it will quickly become a problem if I want to define my own prompt to do the routing with more complexity, or even if I want to structure it for few-shot prompting. I’m not sure what the best solution would be, maybe we could think of a higher-level abstraction that allows us to model a prompt for routing agents and gives the user control over the parts of that prompt. I think the responsibilities of a coordinator should be:
Thinking about that, perhaps we could have a default prompt (or a meta-prompt), but it should be over-writable and modifiable in one place or by overriding a single dependency. |
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I think I have the same problem. I really want to be able to iterate on the prompt (with variables) inside of our prompt UI and get it right for a step, and then use it in the agent. Since ADK changes the prompts (by adding the description, etc), that makes it hard to get the right prompt for a single agent. Now that 9e473e0 is submitted, my previous attempted approach to solve for this is even worse. I was creating a system prompt with context variables for the bits of information I wanted from previous agents in a SequentialAgent flow. Now I get the context from the turn leading into calling this agent, the context is now duplicated, and also the LLMs perform worse with it separately than with the template I had specified directly. Would an appropriate solution be to override the Agent class and override |
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Hello! How are you doing?
As an early adopter of LangChain at the time, I'd like to share a concern.
Today, I spent some time playing around with the ADK, and I really like some of the abstractions introduced.
My concern, however, is that we're currently prompting behind the scenes, especially with the LLMAgent, as if it were a black box. It's well-known that the prompt and the way it's structured are crucial for achieving good results.
As a user, I would feel much more comfortable if we could reach an approach that allows us to explicitly define the prompt instead of delegating it entirely to the ADK. The ADK should help me develop agents but shouldn't hide such important details as prompting.
This is simply my opinion, and I hope we continue developing these kinds of tools that enable us to build amazing products!
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