RAG (Retrieval Augmented Generation)
RAG, or Data Retrieval & Augmentation, is like giving your AI model a superpower to find and use extra information when it needs it.
Why is it needed?
AI models are only as good as the data they're trained on. Sometimes, that data might not be enough to answer a question or complete a task accurately. RAG solves this problem by letting the AI model access and use additional, relevant information from external sources.
Problems without RAG and How RAG Solves Them
Limited Knowledge
Without RAG: Ambiguous queries can be challenging for AI models. If a question has multiple possible interpretations, the model might not know which one to choose.
With RAG: The AI model can use external knowledge to disambiguate the query. For instance, it could search for information about the different meanings of a word to determine the most likely interpretation in the given context.
Handling Ambiguity
Without RAG: Some queries require contextual understanding beyond the immediate text. An AI model might struggle to answer a question that relies on cultural references or domain-specific knowledge it lacks.
With RAG: The AI model can leverage external sources to gain the necessary context. For example, it could search for information about a cultural reference to understand a nuanced question.
Contextual Understanding
Without RAG: AI models trained on static datasets become outdated as the world changes. Information that was accurate at the time of training may no longer be valid. For instance, an AI model trained on product prices from a year ago might give incorrect information due to price fluctuations.
With RAG: The AI model can retrieve up-to-date product prices from the web, ensuring the user receives accurate information.
Stale Information
Without RAG: AI models are confined to the knowledge they were trained on. If a user's query falls outside this scope, the model cannot provide a satisfactory answer. For example, if an AI chatbot is asked about a recent news event it wasn't trained on, it would be unable to respond accurately.
With RAG: The AI model can access external knowledge sources like the internet to find relevant information about the news event and generate an appropriate response.
General Steps to Build a RAG Pipeline
Select Data Sources: Identify the repositories where your AI model will access supplementary information. These sources can include internal databases, external APIs, cloud storage, or web search results. The choice depends on the specific use case and the kind of information needed to augment the model's responses.
Choose a Retrieval Method: Select the strategy your AI model will use to search and retrieve relevant data from the chosen sources.
Keyword Search: This method looks for exact matches of the specified keywords within the data. It's a simple and fast approach but can miss relevant information if the wording is slightly different. Example: Searching for "climate change" will only return results that contain those exact words and might miss articles about "global warming."
Semantic Search: This technique goes beyond keyword matching and considers the meaning and context of words to find relevant information. It can handle synonyms, related terms, and different phrasings.
Example: A semantic search for "climate change" might also return results about "rising sea levels," "greenhouse gas emissions," and "environmental impact."
Embeddings: This approach converts text into numerical vectors (embeddings) that capture the semantic meaning of the words. These vectors can be compared to find semantically similar information, even if the wording is different. Embeddings are often used in conjunction with vector databases, which efficiently store and search for similar vectors.
Example: An embedding for "climate change" might be close to the embeddings for "global warming," "environmental crisis," and "sustainability," allowing the model to find relevant information even if the exact keywords aren't present.
Integrate the Retrieval System: Connect your AI model to the chosen data sources and implement the selected retrieval method. This step often involves using APIs or software libraries to establish communication between the model and the data repositories.
Fine-Tune the Model: Optimize the AI model to effectively utilize the retrieved information. This may involve adjusting model parameters or training the model on specific data to improve its ability to generate accurate and coherent responses that incorporate the retrieved context.
Building a RAG pipeline can be complex and time-consuming. However, UPTIQ AI Workbench simplifies this process by providing a declarative framework that allows developers to define the desired behavior of the pipeline without having to implement the underlying retrieval and integration logic. This abstraction can significantly accelerate the development and deployment of RAG-based applications.
Checkout how you can build RAG pipeline with UPTIQ AI Workbench here
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