Deambiguation

The effect of ambiguity on Generative AI

Why does ambiguity result in AI hallucinations, poor responses and low actionability? The reason is we tend to prompt from an inter-personal communication view point, dependent on relational context. Combining verbal and physical cues, we subconsciously summon the sum total of our experiences with an individual to interpret meaning and intent behind spoken words. AI models are not programmed with human context in mind, resulting in unexpected prompt outcomes called hallucinations.

For example, if you said this statement to another person: I didn't say he stole the money, they would likely understand it within the context of your relationship with them.

But to an AI, every word in that sentence has the potential to imply a level of ambiguity and is meaningless without extensive context:

I didn't say he stole the money

- might imply someone else said it

I didn't say he stole the money

- might imply that I did

I didn't say he stole the money

- might imply that I texted it or emailed it

I didn't say he stole the money

- might imply they or another person

I didn't say he stole the money

- might imply some other action, like borrowed

I didn't say he stole the money

- as the subject, the rest of the sentence means it could be anything else

Fixing Generative AI with Deambiguation

To summarise, generative AI has clarity issues. AI loses accuracy when it has to guess intent rather than knowing it. Better Prompt eliminates this ambiguity with its Deambiguation Language Filters.

Unlike traditional Natural Language Processing (NLP), which passively selects meaning from context, Better Prompt deploys a Deambiguation layer proactively testing alignment across multiple foundational models, up steaming the effect.

"We found the majority of AI errors aren't failures of intelligence, they’re failures of alignment. Models are trained to be agreeable, so they hallucinate specific details to fill vague requests. Our Deambiguation Language Filters intercept vague prompts and align context for precision before the model even generates a response."
— Andy Futcher, Co-Founder of Better Prompt

How does Deabstraction work?

Better Prompt’s Neutral Language Engine uses Deambiguation as a rigorous filter between the user and Large Language Models (LLMs). We are a customer-facing tool where users paste or type prompts in their voice and style. Better Prompt reduces the ambiguous phrasing of the original prompt, translating it into neutral language for direct, factual and unambiguous responses.

Clear up confusion with our language filters. We identify and substitute misinterpreted words, replacing ambiguity with clarity.

10 More reasons to use Better Prompt

Reasons	Betterprompt Neutral Language	Natural Language Prompting
Aligns your prompts to match the highest value scientific training data	Yes	No
Saves you time, saves your tokens and decreases context window usage	Yes	No
Works with your favorite favourite chatbot and improves your AI experience	Yes	No
Helps protect your privacy by filtering sensitive & personal information	Yes	No
Deabstraction technology enhances model reasoning capabilities	Yes	No
Reduces misinterpretation with Deambiguation language filters	Yes	No
Locally stored prompt history is your Incognito mode AI	Yes	No
Reduces the risk of prompt injection and improved AI safety	Yes	No
Rapidly interprets your prompts to level-up your prompt skills	Yes	No
Advocates for you by providing you insight and choice on models	Yes	No
Total reasons to use it?	At least 10	Not many

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