AI Seo Plan – AI Labs

AI-SEO Plan

Building a RAG Prompt for a SEO Chatbot

Challenges and Learnings

Retrieval Augmented Generation (RAG) – I built a custom chatbot that performs a Google search on a keyword, injects the results into a prompt, and generates actionable insights. This innovative approach is both exciting and complex. Here’s our current progress

Current Status

To ensure our SEO plan is based on real Google search results, we built a chatbot that calls the Google Search API, sends data to MS Azure for analysis, and returns actionable insights. We set up an Assistant in MS Azure with the right LLM model and a detailed prompt. As we review results and gather feedback, the agent can be continuously improved.

Key Hurdles

Integrating Search Results: Setting up the AI prompt was not that hard but matching up Google search results required an API call based on the keyword entered and sending the search results to Azure was a challenage.

Google Search Integration: Configuring the chatbot to perform a Google search and extract meaningful results requires robust APIs and accurate parsing mechanisms.

Report Format: The prompt needed to be reworked several time to achieve a consistent result.

What We’re Learning

Publishing a custom chatbot involves understanding multiple systems like Docker, APIs, and cloud deployment. Simplifying the user experience while handling complex backend tasks is a not a straight forward process.

Applications: Retrieval augmented retrieval, RAG ensures models use only the data you provide, avoiding outdated or missing information. Pre-trained models don’t retain real-time data, and crucial business-specific content is often unavailable. By retrieving fresh, relevant information, RAG delivers accurate, up-to-date insights.

Making Advanced Chatbots Accessible

I’m committed to refining the process and sharing our learnings to make advanced chatbots accessible for all. Stay tuned! 🚀

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