Fake It Till You Make It: HyDE, Step-Back Prompts, Hybrid Search, and More
As you may know already,
“There are a lot of issues with vanilla RAG.”
Are you tired of AI giving you half-baked answers? Frustrated with language models that seem to know everything but your specific data?
You’re not alone. While Retrieval Augmented Generation (RAG) promised to bridge the gap between AI and your proprietary information, many are finding that vanilla RAG just isn’t cutting it. The good news is that there are many advanced techniques that can help us get better results!
But before we expose those limitations and show you how to overcome them, let’s make sure we’re all on the same page.
What is RAG, and Why Should You Care?
RAG, or Retrieval Augmented Generation, is a game-changing technique that connects external data sources to Large Language Models (LLMs). It allows you to ask questions about your own proprietary data and get informed responses. In its simplest form, vanilla RAG works by chunking your documents, embedding them, storing them in a vector database, and then retrieving relevant chunks when a user asks a question.
But remember: vanilla RAG is just the tip of the iceberg. Researchers have introduced different types of RAG improvement techniques that here we are going to cover a few of them.
Vanilla RAG is Holding You Back: Here’s Why You Need to Level Up
While vanilla RAG is a great starting point, it falls short in several key areas. For instance, “When your question is let’s say so-called summarization type questions… or when there is a comparison you want to just compare multiple things against each other… vanilla RAG fails to answer them.”
This limitation is just one of many reasons why you need to explore advanced RAG techniques.
Create Fake Questions to Get Real Answers: The Magic of Hypothetical Prompts
One interesting approach is the use of hypothetical questions. Instead of directly passing a user’s query to the vector database, this method generates several relevant questions first.
For instance, “We can generate some hypothetical questions that users may ask in the future and later on when user ask a question we can go and find the most similar hypothetical questions that we have then find the relevant chunks.”
This approach allows for a more nuanced search, potentially uncovering information that a direct query might miss.
Fake It Till You Make It: Hypothetical Document Embeddings
Taking the concept of “fake it till you make it” to a whole new level, hypothetical document embeddings involve generating fake responses to a user’s query without any context. In our video today, Mehdi explained:
“We can generate some fake documents… Now that we have this fake document then we can go and pass this document and find relevant chunks from the vector database that are similar or related to this fake document.”
This technique can help bridge the gap between the user’s query language and the language used in your documents, potentially leading to more relevant retrievals.
Curious to learn more?
In the following video, we also covered -
- step-back prompting,
- sub-queries,
- automatic chunk merging,
- hierarchical indexing and
- hybrid retrieval and reranking…
Watch the full episode here! 🤓:
👇
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