ChatBot.structured_prompt

Configure the chatbot with a structured prompt built from keyword sections.

USAGE

ChatBot.structured_prompt(**sections)

This is a convenience method for quickly building attention-optimized prompts without using the full prompt builder API. It automatically structures the provided sections according to attention-based principles.

Parameters

****sections** : = {}

Keyword arguments defining prompt sections.

Returns

ChatBot

Returns self for method chaining

Recognized Keyword Arguments

  • persona: behavioral role (e.g., "senior developer")
  • task: primary task description
  • constraints: list of requirements or constraints
  • format: list of output formatting requirements
  • examples: dict of input/output examples
  • focus: primary goal to emphasize

Examples

Quick structured prompt creation

import talk_box as tb

bot = (
    tb.ChatBot()
    .model("gpt-4-turbo")
    .structured_prompt(
        persona="senior software architect",
        task="Analyze codebase architecture and identify improvements",
        constraints=[
            "Focus on security vulnerabilities",
            "Identify performance bottlenecks",
            "Suggest specific fixes"
        ],
        format=[
            "Use bullet points for findings",
            "Include code examples",
            "Prioritize by severity"
        ],
        focus="actionable recommendations for immediate implementation"
    )
)

Combining with other configuration

expert_bot = (
    tb.ChatBot()
    .model("gpt-4-turbo")
    .temperature(0.2)
    .structured_prompt(
        persona="expert debugger",
        task="Identify root cause of performance issues",
        constraints=["Provide reproducible test cases"],
        focus="finding the root cause, not just symptoms"
    )
    .max_tokens(1500)
)
⦾