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Series
In this series, we’ll walk through the practical and technical aspects of building a RAG pipeline, with code examples and real-world use cases. Our anchor example will be a project called TalkToPDF, a tool that lets you “chat” with your PDFs.
In the previous section, we saw that Shreya didn’t get the expected response when she asked: Explain the difference between waveguide and coaxial cable in practical applications. The system returned partial matches or generic definitions—not the cr...
In the previous section, we saw that although Shreya made a good improvement in her system and it worked well for a few prompts, it still struggled with complex tasks. In such cases, the LLM was hallucinating and not performing well. Chain of Thought...
In the previous section, we saw that Shreya faced a problem. When she asked a simple question expecting a straightforward answer, she was overwhelmed with too much information and responses she didn't request. To recap, the question was: What was th...
In the last chapter, we saw how Shreya was discouraged by her system's response to one of her questions. She then returned to the drawing board to find ways to improve her system. Let's see what she discovered and whether it solved her issue. Let me ...
Welcome to the first blog of the series RAG—A powerful technique that enhances the accuracy and relevance of responses generated by large language models (LLMs) by incorporating information from external data sources relevant to user’s query. 💡 Pre...
