Build a RAG Pipeline from Scratch in Python: A Step-by-Step Guide

It sounds like you're diving deep into Retrieval-Augmented Generation (RAG) systems, which combine the strengths of vector-based semantic search with large language models to generate contextually relevant responses. You've covered some crucial aspects and challenges of RAG, including how it works and its limitations.

Key Points Recap

1. Semantic Search: By converting both questions and document chunks into vectors using a pre-trained model (e.g., Sentence Transformers), you can perform semantic search to find the most similar documents to the user's query.
2. Prompt Engineering: The way you frame the prompt is critical for guiding the LLM to use only the provided context, thereby reducing hallucinations but not eliminating them entirely.

Addressing Limitations

Hallucination Myth

While RAG significantly reduces the likelihood of hallucinations by providing relevant context, it's important to acknowledge that:

• LLMs can still generate plausible-sounding information: Even when given strict instructions, LLMs might infer or extrapolate based on their training data.
• Contextual guidance is key: By carefully crafting prompts and ensuring they include clear directives (e.g., "Answer using ONLY the context below"), you can mitigate hallucinations to a large extent.

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