Scaling with RAG: The recipe for real-time, accurate responses
von Nadine Noppinger
Language models like ChatGPT have proven to be powerful tools for brainstorming new ideas, writing emails, and tackling various creative tasks. Using these models in their default state—known as zero-shot prompting—offers convenience. However, as exciting as it is to generate quick responses, this approach presents several challenges, especially when attempting to scale up and bring ideas to production.
Key Challenges of Zero-Shot Prompting:
- Hallucinations: The model may present false or fabricated information when it doesn’t have the correct answer.
- Lack of Domain-Specific Knowledge: Training data for these models is generic and not tailored to specific industries or use cases.
- Outdated Information: Models are trained up to a certain date and lack real-time knowledge.
- No Citations/Credits: Responses are often presented without clear sources or references.
- Repetitiveness: The model can become redundant in its responses, offering limited creativity.
These issues become even more problematic when you move from the ideation phase to production, especially when precision and accuracy are required. This is where RAG comes into play.
Example in Cooking
Imagine you're building a cooking assistant (e.g. for FitForFun) that can answer all sorts of recipe-related queries, including dietary restrictions like veganism. Instead of forcing the model to manage all 20 curry recipes in a single context window, a RAG system would take the prompt ("Find me a vegan curry recipe"), search through a vectorized database of all your recipes, retrieve the ones that match the "vegan" query, and then use the language model to generate a human-friendly response.
In this scenario, RAG is solving two key issues:
- Efficiency: By retrieving only the most relevant recipes, you reduce the noise in the prompt and keep the response focused.
- Accuracy: Instead of relying solely on the model's general knowledge, the system can pull precise information from a curated dataset.
What is RAG?
RAG, or Retrieval-Augmented Generation, is a framework designed to overcome the limitations of zero-shot prompting by improving the quality of generated outputs. It works by integrating an external knowledge base into the language model’s response generation process.
In simple terms, RAG takes the user’s input (or prompt), turns it into a search query, and retrieves relevant information from a pre-indexed database before generating a response. This process enhances the accuracy and relevance of the model’s answer by leveraging both the language model’s reasoning abilities and the power of retrieval from a rich, structured data source.
How RAG Works:
- Ingestion: The process of collecting and preparing the data.
- Retrieval: Searching through the external data to find the most relevant pieces of information.
- Generation: Using both the retrieved data and the language model to generate a response.
Challenges in Building RAG Systems
Building a RAG system is complex and comes with several challenges:
- Data Quality: For our cooking example, the data cleaning process involves ensuring that every recipe follows a uniform structure, where the title, ingredients, and instructions are in the right places. This is essential for the vector search to later accurately retrieve relevant information, such as finding all vegan recipes or recipes that use a specific ingredient.
- Data Freshness: Ensuring that the dataset is continuously updated with new records is crucial for relevance. In a cooking assistant, for example, new recipes are frequently created, and the system must be able to ingest fresh content in real time.
- Embedding Accuracy: Choosing the right embedding model is crucial for accurate search results. Embeddings that are too generic might not capture the nuances of your specific domain.
- Latency: Searching through large datasets can introduce delays. Optimizing for speed without sacrificing accuracy is a constant balancing act. Testing various combinations of embeddings models, vector databases and large language models helps in this case.
- Scalability: As your knowledge base grows, so do the complexities of retrieval and ingestion processes. You’ll need to continually update and maintain the system.
- Handling Edge Cases: In any retrieval system, there will be outlier queries that don’t fit well with the rest of the data. Ensuring that the system still provides meaningful responses in these cases is a challenge.
Key Takeaways
- RAG stands for Retrieval-Augmented Generation, a method that improves the accuracy of language models by integrating them with a retrieval system.
- Zero-shot prompting, while useful, has limitations, such as hallucinations, lack of domain-specific knowledge, and outdated information.
- RAG systems combine the reasoning power of language models with the precision of search engines, making them ideal for use cases that require domain-specific knowledge or real-time information retrieval.
- RAG is ideal when you need a system that can scale across vast or specialized datasets without needing to retrain the model frequently.
- And RAG can provide relevant sources or citations in its responses, increasing trustworthiness.
- But building an RAG system involves challenges related to data ingestion, retrieval, and generation. Overcoming these challenges requires thoughtful planning, especially when it comes to data quality and system scalability.