Things to consider when using Claude AI
How does Claude compare to similar AI tools? does it ever go wrong?
AI Tool: Claude
Level: Beginner
Access: Free
This guide is part 2 of the free course Learn how to use Claude.
In this guide, we will cover:
Can Claude make mistakes?
Potential Pitfalls of LLMs
The deeper reason why LLMs make mistakes
Hallucination rates of Claude
Differences between Claude and similar tools
Can Claude make mistakes?
In the previous guide An Introduction to Claude AI we learned about what Claude AI is as a Generative AI LLM, and the impressive capabilities it has.
But can Claude ever make mistakes? The simple answer is, sometimes, yes.
As we leaned in the previous guide, LLMs like Claude have undergone extensive training, enabling them to deliver accurate responses with impressive consistency.
However, it is also important to understand that this technology is relatively new and not infallible can occasionally make mistakes, for a number of reasons.
Potential Pitfalls of LLMs
There are times where LLMs may provide inaccurate or misleading answers, which are called AI hallucinations.
This can happen for a number of reasons:
Ambiguity in Input Queries: When your question lacks clarity, the AI may make inaccurate assumptions, leading to incorrect responses
Gaps in Knowledge: If the required information is absent from the AI's training data, it may struggle to provide accurate answers
Inaccurate information: Some of the information that AI learns from such as some web pages or social media, might have inaccurate or false information.
Design Limitations: AI models are constrained by their inability to access real-time information and understand context beyond their training cut-off date
There is also another, deeper reason why LLMs make mistakes.
The deeper reason why LLMs make mistakes
LLMs don’t live in the world like we do, they don’t have eyes, ears or can smell or taste things. Their world, all LLMs see and understand - is text.
In the previous guide we saw that Generative AI systems are trained to generate human-like content, by learning from the content we humans create such as books, web pages etc.
Learning to generate human-like content is not the same thing as knowing the difference between what is real and true. Fundamentally, LLMs can’t tell the difference between whats real and whats not.
One way we could try to solve this is by ensuring we only give LLMs the most accurate information to train on, so say the highest quality data such as books and academic papers.
Unfortunately, LLMs need huge amounts of data to train on, and high quality data such as books and academic papers is actually not enough data to train a good LLM.
This is why, in practice, most LLMs need to also be trained on lower quality data such as web pages, and social media posts.
As you probably know, unfortunately, not everything said on web pages or social media is always true or accurate information, which is why it is lower quality data, and a source of mistakes when AI learns from them.
So this is an ongoing challenge that means at the moment, there is a risk that LLMs like Claude can make mistakes for a number of reasons.
But how often does Claude make mistakes?
Hallucination rates of Claude
Hallucination rates, which is the rate at which LLMs make mistakes, vary for different LLMs.
The good news is, hallucination rates are generally low, which means most of the time LLMs give an accurate answer. But how often?
Hallucination rates can vary between 2.5%-8.5% for many popular LLMs, but some can be more around 15%.
Claude’s latest and best LLM at time of writing Claude 3 Sonnet has a hallucination rate of 6%, which means you can expect it to make a mistake in accuracy 6% of the time.
These will continue to improve with new versions of Claude.
So what can you do about it now?
The best approach is to always check the factual accuracy, if thats important.
Sometimes you might not need it to be factually accurate, for example if you are asking Claude for names for a fantasy character in a fiction book.
But if you are asking Claude for information that needs to be accurate, its aways best to double check yourself that what Claude has said is correct, bearing in mind it will only make a factual mistake around only 6% of the time.
Differences between Claude and similar tools
While Claude has many similarities to other AI tools like ChatGPT, there are key differences. Sometimes those differences are important, in helping you decide which AI tools to use.
So what are key differences between Claude and other similar AI tools?
Model Performance and Updates: Claude Opus 3 is currently considered the best-performing AI model, surpassing ChatGPT's GPT-4.
Feature Sets and Capabilities: ChatGPT has a more comprehensive feature set, but Claude is continually evolving and adding new features, minimising the differences between them.
Ethical Guidelines and User Expectations: Claude adheres to the Constitutional AI guidelines, ensuring its outputs align with ethical standards and user expectations, which may differ from the principles guiding other LLMs.
Transparency and Explainability: Claude prioritises transparency and explainability, providing more insight into its decision-making processes and outputs.
Conversation Handling: Claude excels in handling nuanced and context-dependent conversations, allowing for more human-like interactions.
Development Focus: Claude's development is focused on ensuring its outputs are reliable, trustworthy, and unbiased, which may not be the primary focus of other LLMs.
However its important to bear in mind in the fast moving world of AI, these features and abilities will continue to change.
In the next guide, we get stuck in to how to use Claude for everyday tasks.
Next in this course: Using Claude for personal tasks