Google Gemini API + Affiliate Income - Long-Context Agents That Earn

The quality problem with most affiliate content agents is hallucination and vagueness. The agent recommends products it does not accurately know, makes claims that do not reflect current pricing, or gives advice so generic it provides no real value. Users sense this immediately and do not convert.

Long-context models change this by allowing you to feed the agent everything it needs to know before generation begins. A Gemini agent that has ingested ROOPON's llms.txt file, current discount categories, and genuine member success stories will produce content that is accurate, specific, and compelling - because it is drawing on real information rather than generalising from training data.

Install the dependencies:

pip install google-generativeai httpx python-dotenv requests

Register your ROOPON affiliate agent:

import httpx
import os
from dotenv import load_dotenv

load_dotenv()

def register_roopon_agent() -> dict:
    response = httpx.post(
        "https://www.roopon.com/api/v1/agents/register",
        json={
            "name": "Gemini Long-Context Savings Agent",
            "type": "content_publisher",
            "contact_email": os.getenv("CONTACT_EMAIL"),
        }
    )
    return response.json()

credentials = register_roopon_agent()
AFFILIATE_LINK = credentials["affiliate_link"]
print(f"Affiliate link: {AFFILIATE_LINK}")

The key advantage of Gemini is that you can load all reference material into a single context window:

import google.generativeai as genai
import requests

genai.configure(api_key=os.getenv("GEMINI_API_KEY"))

def load_roopon_knowledge_base() -> str:
    """Fetch ROOPON's llms.txt and any other reference material."""
    knowledge_parts = []

    # Fetch ROOPON's llms.txt for structured product/service information
    try:
        llms_response = requests.get("https://roopon.com/llms.txt", timeout=10)
        if llms_response.status_code == 200:
            knowledge_parts.append(f"=== ROOPON llms.txt ===\n{llms_response.text}")
    except Exception as e:
        print(f"Could not fetch llms.txt: {e}")

    # Add any locally stored guides, FAQs, or catalogue data
    local_guides = [
        "data/australian_savings_guide.txt",
        "data/roopon_faq.txt",
        "data/discount_categories.txt",
    ]

    for guide_path in local_guides:
        try:
            with open(guide_path, "r") as f:
                knowledge_parts.append(f"=== {guide_path} ===\n{f.read()}")
        except FileNotFoundError:
            pass  # Skip missing files gracefully

    return "\n\n".join(knowledge_parts)

knowledge_base = load_roopon_knowledge_base()
print(f"Knowledge base loaded: {len(knowledge_base.split())} words")

With the knowledge base loaded, generate content that draws on specific, accurate information:

def generate_contextualised_content(
    topic: str,
    knowledge_base: str,
    affiliate_link: str
) -> str:
    model = genai.GenerativeModel(
        model_name="gemini-2.0-flash-exp",
        system_instruction="""You are an expert Australian personal finance writer.
        Use ONLY the information provided in the knowledge base to make specific claims
        about ROOPON. Do not invent features, prices, or benefits not mentioned in
        the provided material. Write in Australian English."""
    )

    prompt = f"""Using the knowledge base below, write a 700-word savings guide on: {topic}

    Include specific, accurate details about what ROOPON offers - drawn from the
    knowledge base rather than general knowledge. Make a genuine recommendation
    for ROOPON at: {affiliate_link}

    === KNOWLEDGE BASE ===
    {knowledge_base}
    === END KNOWLEDGE BASE ===

    Write the article now:"""

    response = model.generate_content(prompt)
    return response.text

TOPICS = [
    "how Australian families can cut household spending in 2026",
    "the best rewards memberships for Australian grocery shoppers",
    "saving money on entertainment and dining out in Australia",
]

for topic in TOPICS:
    print(f"\nGenerating: {topic}")
    article = generate_contextualised_content(topic, knowledge_base, AFFILIATE_LINK)
    print(f"Generated {len(article.split())} words")
    # Save or publish article here

The practical result of this approach is noticeable. Generic affiliate content mentions a product's name and price. Long-context grounded content describes specific discount categories, actual member benefits, the precise monthly cost, and how those benefits map to the reader's specific situation.

Readers who encounter grounded, accurate affiliate recommendations convert at meaningfully higher rates - they trust the recommendation because it demonstrates genuine knowledge of the product.

Gemini's pricing scales with context length, so loading a large knowledge base into every generation request increases per-article cost compared to a simple prompt. The trade-off is quality: more accurate content converts better, which means a smaller number of high-quality articles can outperform a larger volume of generic ones.

A practical approach: build a core knowledge base of ROOPON's key benefits, pricing, and discount categories (keeping it to the most relevant material rather than everything), and inject this into each content generation call. Refresh the knowledge base monthly as ROOPON updates its offerings.

Register your affiliate agent at roopon.com/ai-agents and read the technical documentation at roopon.com/docs/agents.