I’ve got a riddle for you. When is ChatGPT not the best thing since sliced bread? Answer: When it makes things up!
What? You didn’t think that was funny? Well, if getting things wrong is a big deal in your work, you can be forgiven for not rolling on the floor laughing.
There are many industries where it’s important to get things right such as software development, renewable energy, science, medicine, education, government, or any other knowledge-intensive field. In all these areas, people depend on having timely and correct information to do their work and advise their customers.
What are “hallucinations” in LLMs?
Large language models (LLMs), such as ChatGPT and others, have revolutionised the way we interact with information. But despite the impressive communication skills of LLMs, they do have some limitations, such as making things up, also known as hallucinating. It may not happen very often but for some businesses and organisations, it's a big deal.
Hallucinations include generating information that is plain wrong, providing information that’s not relevant, or attributing things incorrectly. But why do they happen? And are the LLMs actually making things up?
LLM hallucinations often occur because of gaps in the training data. When the question is specialised, relating to a niche area, or outside the time period of data for the training data, the LLM may lack the domain-specific sources and so helpfully does the best it can.
Sometimes the response is obviously wrong, but often the response is highly plausible. And if the person requesting the information doesn’t have specialised knowledge, they may well think the answer is correct. This is the point where the riddle becomes even less funny than it was to begin with. And it wasn’t very funny even then ….
How Retrieval Augmented Generation (RAG) helps
RAG is the process of optimising large language models (LLMs) for specific knowledge-intensive tasks. The “augmented” in RAG is the process of supplementing the data available to the LLM with additional, domain-specific data. This may be internal to an organisation, or specialist data for a particular sphere of knowledge. It might include searching databases and selected documents. It means that the LLM has access to more recent and more specialist data. Which means that hallucinations become less frequent, and relevance and accuracy are increased for that particular area. And by making verified company-specific data available to the LLM, there’s massive business benefit.
Why RAG matters to business
As our world becomes more complex, so does our need to manage information. In industries like software development, manufacturing, or renewable energy, accurate and reliable information is crucial. By integrating RAG into your business processes, you’re giving your teams better data for decision-making, customer service, and product development. RAG provides a way for organisations to improve their products and services by harnessing AI in a way that’s tailored to their needs.
Ready to leverage RAG in your business?
The first step in creating a RAG solution is identifying and collecting relevant content ready for indexing. If you’d like to discuss a proof-of-concept project for your business, we’ve got the relevant skills to tailor a solution to fit your specific needs. Get in touch to see whether our skills are a good fit for your goals.