AI-Driven LinkedIn Post Generator: Powered by Llama 3.2, LangChain, Streamlit, and Groq Cloud
Keywords:
AI for personal branding, AI-powered automation, Generative AI, Groq cloud, LangChain, LinkedIn content generation, Llama 3.2, Natural Language Generation (NLG), Professional networking, StreamlitAbstract
In today’s fast-paced digital world, professionals and businesses are expected to stay active on platforms like LinkedIn but consistently writing engaging, thoughtful posts can be a challenge. This project introduces an AI-powered LinkedIn Post Generator that helps users turn ideas into professional- quality posts in just a few clicks. Built using Llama 3.2, a powerful open-source language model, and orchestrated with LangChain for smart prompt management, the app provides customized content generation based on user inputs such as topic, tone, and audience. The front-end, developed in Streamlit, offers a clean, easy-to-use interface, while Groq Cloud ensures lightning-fast performance in generating posts. Whether someone wants to share a career milestone, give advice, or comment on a trending topic, this tool provides a reliable way to create polished, personalized content instantly. By combining the latest AI technologies with an intuitive design, this project offers a practical solution for anyone looking to boost their presence on LinkedIn without spending hours writing and editing.
References
H. Touvron, L. Martin, K. Stone, P. Albert, “Llama 2: Open Foundation and Fine-Tuned Chat Models,” Arxiv.Org, Jul. 19, 2023, Available: https://arxiv.org/abs/2307.09288
LangChain Documentation. LangChain: Building Applications with LLMs through Composability, Available: https://python.LangChain.com/docs/introduction/
Streamlit, “The fastest way to build and share data apps,” Streamlit.Io, 2025, Available: https://streamlit.io/
Groq, “Products - Groq is Fast AI Inference,” Groq, Oct. 18, 2021, Available: https://groq.com/products/
Y. Bang, S. Cahyawijaya, N. Lee, W. Dai, “A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity,” Arxiv: 2302.04023 [cs], Feb. 2023, Available: https://arxiv.org/abs/2302.04023
T. B. Brown, B. Mann, N. Ryder, “Language Models Are Few-Shot Learners,” arxiv.org, vol. 4, no. 33, May 2020, Available: https://arxiv.org/abs/2005.14165
R. Bommasani, “On the Opportunities and Risks of Foundation Models,” Arxiv: 2108.07258, vol. 1, no. 1, Aug. 2021, Available: https://arxiv.org/abs/2108.07258
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing,” ACM Computing Surveys, vol. 55, no. 9, Sep. 2022, doi: https://doi.org/10.1145/3560815.
J. Wei, X. Wang ,D. Schuurmans, “Chain of Thought Prompting Elicits Reasoning in Large Language Models,” Arxiv: 2201.11903 [cs], Oct. 2022, Available: https://arxiv.org/abs/2201.11903
OpenAI, “Best practices for prompt engineering with the OpenAI API | OpenAI Help Center,” Openai.com, 2024, Available: https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api
