#retrieval-augmented-generation

#retrieval-augmented-generation

Large language models (LLMs) base their predictions on training data and cannot respond effectively to queries about other data. The AI industry has dealt with that limitation through a process called retrieval-augmented generation (RAG), which gives LLMs access to external datasets. Google's AI Overviews in Search, for example, use RAG to provide the underlying Gemini model with current, though not necessarily accurate, web data.

Retrieval-augmented generation (RAG) has quickly become the enterprise default for grounding generative AI in internal knowledge. It promises less hallucination, more accuracy, and a way to unlock value from decades of documents, policies, tickets, and institutional memory. Yet while nearly every enterprise can build a proof of concept, very few can run RAG reliably in production. This gap has nothing to do with model quality.

Retrieval Augmented Generation Chatbot uses a knowledge base to answer questions about information the model has no clue about. The knowledge base usually is an organization's internal documentation. The model has never seen the documentation during the training process. The documentation is given to the model as a context inside the prompt, and therefore, the model is able to answer questions correctly.

We want people to be able to ask questions as they would in everyday life; for example: 'I've just had a baby, do you know what help I can get?'

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.