#retrieval-augmented-generation

#retrieval-augmented-generation

The Tribune alleges that its lawyers contacted Perplexity in mid-October asking if the AI search engine was using its content, according to the complaint. Perplexity's lawyers replied it did not train models with the Tribune's work, but that it "may receive non-verbatim factual summaries," the lawsuit claims. The Tribune's lawyers, however, argue that Perplexity is delivering Tribune content verbatim. Interestingly, the newspaper's lawyers are also calling out Perplexity's Retrieval Augmented Generation (RAG) as a culprit.

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Most chatbots fail for one simple reason: they ignore what's actually happening with the user. Imagine asking for help while browsing your account page, but the bot replies with a generic FAQ. It's frustrating because it ignores details like where you are in the app, what plan you're on, or what you just did. That's where context comes in. When a chatbot understands those details, it stops feeling like an obstacle and starts acting like a real assistant.

Retrieval Augmented Generation (RAG) combines the power of Large Language Models (LLMs) with a custom knowledge base, enabling precise and contextually relevant responses from customers.

Fine-tuning provides consistent and fast responses, but requires lengthy retraining for updates, while RAG offers instant updates but involves handling latency and interface challenges.

The allure of large context windows in AI models promises vast data handling and perfect recall, but reality often diverges from these enticing features.