Builds a Retrieval Augmented Generation pipeline using a pre-made blueprint with Python.
Automations like: extracting product reviews from Amazon and summarizing them for a marketing report.
Saves time by providing a pre-made foundation for your AI pipeline, and reduces the barrier to entry for building complex AI models.
"A founder of an e-commerce startup wants to automatically generate sales summaries from customer reviews on Amazon. They create a new instance of the RAG pipeline, connect a dataset of Amazon reviews, and use the model to summarize the reviews. After a day, they have a comprehensive market summary to inform their business decisions."
Pick up this skill when you're new to AI development and want a structured approach.
Senior engineers will reach for this when they need to quickly build a complex AI pipeline.
Don't be confused โ RAG does not replace human evaluation, but instead supports humans in augmenting their work with AI outputs.
Rag provides a foundational pipeline for Retrieval Augmented Generation, allowing startup founders to generate text based on relevant information retrieved from a database or knowledge base.
python main.py --config config.json --input "What is the capital of France?" --output generated_text.txt
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