These 6 Simple Tips Will Improve Your RAG Accuracy

Have you wondered why AI sometimes fails at basic tasks? AI models are trained on vast datasets. But even the best models struggle with topics outside their training data. Retrieval Augmented Generation, or RAG, can bridge these gaps. RAG helps your AI provide accurate, relevant responses every time.

You might be surprised to learn just how easily RAG can transform your AI’s capabilities. And how quickly things can go wrong!

Why AI Needs RAG

Large language models, or LLMs, know about a lot of topics. But they don’t know everything. They don’t know about today’s news. They don’t know about your company’s internal data either.

This is where RAG comes into play. RAG connects your LLM to the data it needs to answer questions on these topics. It does this by retrieving helpful information for the LLM. It then sends that context to the LLM inside a prompt.

This process is surprisingly easy to understand, as we’re about to see.

How RAG Works?

RAG involves three main steps:

Retrieval: The RAG application retrieves relevant data. This can be from a vector database or other knowledge sources. The context may come from a set of documents like customer support transcripts or handbooks. It might also come from SaaS platforms or knowledge bases.

Augmentation: The retrieved data is then used to augment the AI’s prompt. This provides the LLM with context it doesn’t have on its own.

Generation: The AI generates a response. These responses are more accurate because of the additional context. This process allows the LLM to respond to topics it wasn’t trained on.

Understanding the fundamental steps is important. But to achieve accurate results from RAG you’ll need the right tools.

Tip 1: Leverage Vector Databases for Semantic Search

Semantic search lets you find text with similar meaning. Vector databases store text as vectors. These vectors represent the meaning of a piece of text. This makes it easier to find relevant information. Semantic search can find similar text, even when the wording is different.

Semantic search can be powerful. But if you pick the wrong embedding model it will all fall apart. That’s why this next section is so important.

Tip 2: Choose the Right Embedding Models

Embedding models are a special type of machine learning model. They allow you to turn a piece of text into a vector. A vector is just a list of floating point numbers (numbers with a decimal). These numbers represent the meaning of a piece of text.

Examples of embedding models include OpenAI’s text-embedding-v3-large. This model can capture the subtleties of language. This helps your AI understand complex queries and respond accurately.

The choice of embedding model can make or break your RAG application. But an embedding model won’t do you any good if you don’t do this next part.

Tip 3: Chunk Your Text the Right Way

Embedding models can work with pieces of text up to a maximum size. Some of your documents will be bigger than this maximum size. When that happens, you need to chunk them.

Chunking a document breaks it up into smaller pieces. Depending on the content, different chunking strategies may work better than others. You can chunk your documents into paragraphs or by section. You can also use more advanced approaches that rely on natural language processing.

If you mess up your embedding model or chunking strategy, your RAG application is going perform poorly. Luckily, there’s an easy way to find a configuration that will work for you.

Tip 4: Use a RAG Evaluation Tool

Picking the best embedding model and chunking strategy is not easy. Benchmarks can provide a good starting point, but your data is unique.

Trying different strategies can be time-consuming. Writing python scripts to populate vector indexes can take days. Deciding which vector index performs best requires a lot of complex data analysis.

This is where a RAG evaluation platform can be a life saver. Tools like Vectorize allow you to automate this work. You can try different embedding models and chunking strategies. This analysis lets you know for sure which strategy will work best for you. And it only takes a minute or two to run.

But even the best vector indexes are useless if they’re not kept up to date.

Tip 5: Keep Your Data Fresh and Relevant

An outdated database is the enemy of accurate AI responses. Making sure your vector database stays up to date is important. If not, your users may get bad information from your LLM.

This is why you need a RAG pipeline. A RAG pipeline ingests knowledge from sources in your company. This knowledge usually comes from unstructured data. This can be files or documents. But many times it’s pulled from databases or SaaS platforms. A good RAG pipeline will ingest data AND keep it in sync.

For example, your sales team might want to ingest data from Salesforce. You can perform an initial load into your vector database. As your sales team enters new notes, your vector database gets out of date. A RAG pipeline can watch for changes. When it sees one, it can update your vector database. This allows your sales team to ask the LLM questions and get up-to-date answers.

RAG pipelines can improve performance and reduce incorrect responses. But some RAG pipelines are better than others. What exactly makes them better?

Tip 6: Handle Errors Gracefully

Errors are going to happen in any data pipeline. Your RAG pipeline is no different. Source systems will be down. Vector databases will crash. The embedding model API will become unreachable.

A bad RAG pipeline will break when these things happen. A good RAG pipeline will be resilient. When errors happen, you want them to resolve on their own. You don’t want to babysit your RAG pipeline every time there is a hiccup.

This starts with sensible retry logic. When an error occurs, you don’t want to simply give up. You would prefer to try again.

Sometimes, the problem is momentary. The next time you try, things may work. Other times the problem may be more complex. If your database is under load, requests may start to time out. You don’t want to worsen the problem by retrying your request over and over.

Mechanisms like exponential back off can be helpful in these cases. With this approach, you wait longer and longer between retries. This gives the struggling system time to correct.

If something keeps failing, you will eventually want to give up. This is known as dead lettering. Dead lettering gives you a way to clear out problematic updates. It also gives you a way to track when some unsolvable problem occurs.

Future-Proof Your AI Strategy with Vectorize

RAG is not just a tool, but a strategic approach that can unlock the value of AI for your company. But only if you have the right tools in your toolbox.

Vectorize is a purpose-built platform for RAG. Best of all, you can use the RAG evaluation capabilities and create a basic pipeline completely for free. To see how much better your RAG application can be, sign up for a free account and try Vectorize now.