Inspiration
Multiplatforms at one platform. The idea came from a simple problem: creating content for multiple social media platforms usually means rewriting the same idea again and again. I wanted to build one platform where a single query could generate platform-specific content for LinkedIn, Instagram, X, Facebook, and Threads.
What it does
One query for every platform. The MultiPlatform RAG Post Generator takes a document, PDF, URL, or source content and generates tailored posts for five platforms. Each post follows the platform's own tone, length, formatting, and hashtag conventions instead of simply copying the same content everywhere.
How I built it
I built the system using a RAG pipeline. Source documents and URLs are extracted, chunked with configurable overlap, converted into local embeddings using sentence-transformers, and stored in a FAISS IndexFlatIP vector index. L2 normalization makes inner-product search equivalent to cosine similarity.
For generation, only the most relevant chunks are retrieved and provided as grounding context to Llama 3.3 70B through Groq. The frontend was built with Streamlit, with platform previews and a grounding view to make the generated content more transparent.
Challenges I ran into
The biggest challenge was making the generated content feel genuinely different for each platform while keeping the original information accurate. I also had to handle document and URL ingestion, chunking, retrieval quality, structured JSON generation, and making sure the LLM did not receive unnecessary source content.
Another challenge was moving the complete pipeline from a Google Colab prototype into a production-ready Streamlit application without changing the core RAG logic.
Accomplishments that I'm proud of
I'm proud that the project doesn't just generate five versions of the same post—it creates five platform-aware posts from a single query.
I'm also particularly proud of the platform preview and grounding view, which allow users to see how each post will look and understand where generated content comes from. Building the complete pipeline from ingestion to retrieval to generation was another major accomplishment.
What I learned
I learned how important retrieval quality is for reliable generation. I also gained practical experience with FAISS, local embeddings, cosine similarity, document/URL ingestion, structured LLM outputs, and platform-specific prompting.
Most importantly, I learned that building an AI application is not only about getting an LLM to generate text,it is about designing the entire pipeline around grounding, consistency, transparency, and usability.
What's next for Multiplatform Social Media RAG Post Development.
The next step is to make the system more production-ready by adding automation for content generation and publishing, more social platforms, better retrieval evaluation, improved platform-specific controls, content scheduling, analytics, and stronger personalization. The goal is to move from simply generating posts to creating an end-to-end automated social media content workflow.
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