prompts.md

When designing agent specialization through system prompts, the choice between hardcoding prompts at setup and dynamically generating them at runtime often depends on the required flexibility, interpretability, and control.

Strategy 1: Hardcoded System Prompts (Static)

• Use Case: When agents serve stable, well-defined roles.
• Advantages:
  • Easy to audit and debug.
  • Predictable behavior.
  • Simpler deployment.
• Disadvantages:
  • Less adaptive to user-specific or context-specific needs.

Example:

summarizer_prompt = "You are a professional summarizer. Always produce concise, accurate summaries."
llm_summarizer = LLM(prompt=summarizer_prompt)

Strategy 2: Dynamic Prompts (Context-Aware)

• Use Case: When the agent’s role or task details shift based on runtime data, user input, or evolving goals.
• Advantages:
  • Highly adaptable to complex or fluid task environments.
  • Enables intelligent task personalization.
• Disadvantages:
  • Harder to predict behavior.
  • Requires careful input sanitization and prompt engineering.

Example:

def generate_prompt(user_goal):
    return f"You are an expert assistant helping the user achieve the goal: '{user_goal}'. Provide actionable and concise guidance."

llm_agent = LLM(prompt=generate_prompt(user_input))

Hybrid Strategy: Static Core + Dynamic Augmentation

• Combine a stable role definition with runtime context injection.
• Keeps behavior interpretable while increasing flexibility.

Example:

base_prompt = "You are a legal advisor."
def contextualize(base, context):
    return base + f" The current topic is: {context}"

llm_legal_agent = LLM(prompt=contextualize(base_prompt, user_topic))

Architecture Diagram

graph TD
  A[User Input] --> B[Prompt Generator]
  B --> C{Use Dynamic?}
  C -- Yes --> D[Generate Contextual Prompt]
  C -- No --> E[Use Static Prompt]
  D --> F[Specialized Agent LLM]
  E --> F1[Specialized Agent LLM]
  F --> G[Response to User]
  F1 --> G

Loading

This diagram illustrates how a prompt-generation system can choose between static and dynamic prompt construction paths before routing the prompt to the specialized agent.