Tracking AdTech Trends with RAG + Gemini

 

Tracking AdTech Trends Smarter with RAG + Gemini

In the fast-paced world of AdTech, new trends, technologies, and strategies emerge daily. Staying informed means digging through countless articles, blogs, and updates : a task that’s time-consuming and often overwhelming.

What if you could ask a simple question like, "What does adexchanger tells about CTV advertising trends?" and instantly get a summary from recent articles across trusted AdTech sources?

That’s exactly what we built - a smart, AI-powered application using RAG (Retrieval-Augmented Generation) and Google’s Gemini model to help you stay ahead of the AdTech curve.

Step 1: Gathering Content from Multiple Sources

We start by collecting articles from leading AdTech websites like AdExchanger and Adweek. The app crawls and extracts content from different sections, ensuring coverage across key topics like CTV, programmatic advertising, privacy, and more.

These articles are cleaned and stored in a searchable format, becoming the app’s internal knowledge base.

Step 2: Semantic Embedding with Gemini

To make articles searchable by meaning, we use embeddings, i.e. vector representations of content. Google’s Gemini model generates these embeddings, allowing the app to understand context, not just keywords.

This lets us match user queries to the most relevant parts of the content — even if the wording doesn’t exactly match.

Fig. - Code snippet of embedding using Gemini API

Step 3: Retrieval + Few-Shot + Generation

When you ask a question, the app follows a powerful RAG pipeline:

  1. Retrieve: It searches the embedded database for the most relevant article snippets.

  2. Augment: The top results are passed to Gemini along with your question.

  3. Few-Shot Prompting: To guide Gemini’s tone and structure, we include carefully designed examples (few-shot prompts) showing how it should answer.

  4. Generate: Gemini replies with a clear, helpful response using the retrieved content.


Fig. - Code snippet for Prompt containing few shots prompts + documents retrieved from Chroma DB

By combining retrieval with few-shot prompting, we get responses that are not only accurate, but also structured the way we want: conversational, complete, and easy to understand.

Step 4: Clear, Non-Technical Insights

We designed the app for a non-technical audience. Whether you’re a marketer, analyst, or executive, the app breaks down complex AdTech topics into easy-to-grasp insights, no jargon, no fluff.

The Road Ahead: What's Possible Next?

While this first version already offers real value, there’s a lot more we can unlock:

  • Multi-source fusion: Expand from one website to dozens,  including press releases, earnings calls, or even Reddit and LinkedIn discussions.

  • Daily auto-updates: Add background pipelines that crawl and embed new articles in real time.

  • Visual + chart summaries: Combine text-based results with charts or trend graphs pulled from structured data sources.

  • Personalization: Tailor responses based on user preferences, for example, CTV-focused insights vs. privacy regulation updates.

  • Voice + chatbot interface: Integrate with Slack, WhatsApp, or voice assistants for even easier access.

Why It Matters

This isn’t just a research tool,  it’s your AI-powered AdTech analyst. It saves time, keeps you updated, and helps you make better decisions by surfacing the insights that matter.

And with the combination of RAG, Gemini embeddings, and few-shot prompting, it’s built to scale, ready to evolve as the AdTech world does.

Project Link: https://www.kaggle.com/code/silentkiller/adtech-master-2

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