🎯 What You'll Learn

📋 Before You Begin

Table of Contents

⚡ Intermediate ⏱ ~2 min

The Logic of Your New Workflow

By collecting names (e.g., David Ogilvy, Gary Halbert) and frameworks (e.g., AIDA, PAS, BAB), you are creating a Modular Prompting System. Instead of just looking for "similar ads," the bot selects a Strategy first, then a Voice, and then uses your Ads DB for few-shot examples.

The Modular Flow

Here is the logical sequence your Python script should follow:

User provides a product (e.g., "Deep Learning course for Ecologic").
The bot searches your Framework DB. It decides: "Since this is a new technical course, the PAS (Problem-Agitate-Solution) framework works best to address the fear of becoming obsolete."
The bot searches your Persona DB. It decides: "To sound like Ecologic, use the 'Eco-Visionary' persona (inspired by the 'Scientific yet Hopeful' writing style of successful green-tech founders)."
The bot searches your Ads DB for 2-3 examples that already used PAS or the 'Eco-Visionary' tone.
The LLM receives: The Framework Blueprint (How to structure the ad), The Persona Profile (Which words to use/avoid), and The Shot Examples (Real data for formatting).
⚡ Intermediate ⏱ ~2 min

End-to-End Implementation Script

This Python script implements the modular flow with Framework DB, Persona DB, and the orchestrator class.

python 
import pandas as pd
from typing import Dict

# KNOWLEDGE BASE: The 'Brain' of Ecologic

# 1. Framework DB: The structural logic
FRAMEWORKS = {
    "PAS": "Problem: Identify a tech pain point. Agitate: Explain the environmental/career cost. Solution: Present Ecologic.",
    "AIDA": "Attention: Bold hook about Green AI. Interest: Course details. Desire: Future-proof career. Action: Join Ecologic.",
    "BAB": "Before: Inefficient, dirty code. After: Clean, optimized ML models. Bridge: Ecologic's Deep Learning course."
}

# 2. Persona DB: The 'Voice' of the experts
PERSONAS = {
    "The Eco-Visionary": "Scientific, optimistic, authoritative but accessible. Uses words like 'efficiency', 'future-proof', 'balance'.",
    "The Pragmatic Coder": "Direct, no-nonsense, focused on performance and low-latency. Mixes Hinglish for relatability."
}

# 3. Famous Names (Historical Knowledge)
EXPERT_MODELS = {
    "Ogilvy Style": "Story-driven, high-information, focuses on the 'Big Idea'.",
    "Halbert Style": "Direct response, high urgency, emotional hooks."
}

class EcologicOrchestrator:
    def __init__(self, ad_database_path):
        self.ads_db = pd.read_csv(ad_database_path)

    def generate_strategy(self, product_info: str, platform: str):
        framework_key = "PAS" if "course" in product_info.lower() else "AIDA"
        persona_key = "The Eco-Visionary" if platform == "LinkedIn" else "The Pragmatic Coder"
        
        return {
            "framework": FRAMEWORKS[framework_key],
            "persona": PERSONAS[persona_key],
            "framework_name": framework_key
        }

    def assemble_prompt(self, product_info: str, strategy: Dict):
        examples = self.ads_db.head(2)['text'].tolist() 

        prompt = f"""
        SYSTEM ROLE: You are an AI Ad Agent for 'Ecologic'.
        WRITING PERSONA: {strategy['persona']}
        FRAMEWORK TO FOLLOW: {strategy['framework']}

        ### EXAMPLES OF SUCCESSFUL ECOLOGIC ADS:
        1. {examples[0]}
        2. {examples[1]}

        ### YOUR TASK:
        Create a new ad for: {product_info}
        
        Ensure the ad reflects the 'Ecologic' mission: [Logic = Computer, Compute for Eco-friendly software].
        """
        return prompt
Usage 
orchestrator = EcologicOrchestrator("indian_ad_metadata.csv")
strategy = orchestrator.generate_strategy("6-month ML Course", "LinkedIn")
final_prompt = orchestrator.assemble_prompt("6-month ML Course", strategy)
⚡ Beginner ⏱ ~1 min

Do You Still Need Semantic Induction?

Yes, but its role changes. Instead of labeling the data, you use semantic induction to:

⚡ Advanced ⏱ ~2 min

Building the Knowledge Base

Instruct your team to collect data in three distinct buckets:

Framework Database (The "Skeleton")

Problem-Agitate-Solution
Best for: Technical courses, overcoming inertia
Logic: 1. Problem: High energy cost of AI. 2. Agitate: Carbon taxes and compute waste. 3. Solution: Ecologic's Green-LLM course.
Before-After-Bridge
Best for: Inspirational ads, career switchers
Logic: 1. Before: Clunky, energy-draining code. 2. After: Sleek, 'Earth-First' neural networks. 3. Bridge: Our ML for Sustainability module.

Persona Database (The "Voice")

The Eco-Architect

Traits: Visionary, Precise, Hopeful
Keywords: Equilibrium, Optimization, Legacy, Balance
Target: Experienced Developers, Researchers

The Pragmatic Hacker

Traits: Fast, Relatable, Performance-obsessed
Keywords: Lag-free, Efficient, Low-carbon, Scalable
Target: Students, Indian Tech Youth

Vector Matching Implementation

This script uses Vector Matching to find the right Framework and Persona for any given task.

python 
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
import faiss

class EcologicIntelligence:
    def __init__(self):
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
        
        # Knowledge Base: Personas & Frameworks
        self.frameworks = {
            "PAS": "Problem-Agitate-Solution: Focus on the high energy cost of AI and the need for efficiency.",
            "AIDA": "Attention-Interest-Desire-Action: Focus on the prestige of being a 'Green AI' expert.",
            "BAB": "Before-After-Bridge: Contrast messy, high-waste code with clean, sustainable systems."
        }
        
        self.personas = {
            "The Eco-Architect": "Authoritative and visionary. Uses terms like 'sustainable infrastructure'.",
            "The Pragmatic Hacker": "Mixes Hinglish, focuses on performance and efficiency."
        }

    def select_strategy(self, user_query):
        if "career" in user_query.lower():
            return "BAB", "The Pragmatic Hacker"
        return "PAS", "The Eco-Architect"

    def generate_final_prompt(self, query, platform="LinkedIn"):
        f_key, p_key = self.select_strategy(query)
        framework_guide = self.frameworks[f_key]
        persona_guide = self.personas[p_key]
        
        full_prompt = f"""
        ### IDENTITY
        Institute: Ecologic [Compute for Eco-friendly software]
        Persona: {p_key} ({persona_guide})
        Framework: {framework_guide}

        ### NEW TASK
        Platform: {platform}
        Target Course: {query}
        
        Write the ad copy following the framework exactly:
        """
        return full_prompt

ecologic_bot = EcologicIntelligence()
final_prompt = ecologic_bot.generate_final_prompt("6-month LLM Specialization focusing on efficient training")
print(final_prompt)

Key Takeaways