An intelligent content generation system that leverages few-shot learning with Large Language Models (LLMs) to generate high-quality, style-consistent LinkedIn posts based on user-defined parameters such as topic, tone, and length.
This project implements a few-shot prompt engineering pipeline where the model is guided using curated examples from real-world LinkedIn posts. By dynamically selecting relevant examples, the system ensures that generated content closely matches desired writing styles and engagement patterns.
Unlike generic text generators, this system focuses on style conditioning and controlled generation, making it suitable for professional content creation.
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🔹 Few-Shot Learning Pipeline
- Dynamically injects example posts into prompts to guide LLM output
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🔹 Style-Aware Content Generation
- Mimics tone, structure, and engagement patterns of real LinkedIn posts
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🔹 Parameterized Generation
- Supports control over:
- Topic
- Tone
- Length
- Supports control over:
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🔹 Data-Driven Prompting
- Uses structured dataset of posts for better contextual grounding
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🔹 Interactive UI
- Built using Streamlit for real-time post generation
User Input (Topic, Tone, Length)
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Example Retrieval (Few-Shot Selection)
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Prompt Construction (Examples + Instructions)
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LLM (via API)
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Generated LinkedIn Post
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Streamlit UI
- Language: Python
- LLM Integration: Groq / OpenAI API
- Framework: Streamlit
- Prompt Engineering: Few-shot learning
- Data Processing: Pandas / JSON handling
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User provides:
- Topic
- Tone
- Desired length
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System:
- Filters and selects relevant example posts
- Constructs a few-shot prompt
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LLM:
- Generates a post based on examples and instructions
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Output:
- High-quality LinkedIn-ready content
Integrate vector database (RAG + few-shot hybrid)
Add engagement scoring model
Introduce multi-agent refinement pipeline
Build FastAPI backend for production deployment