Inferring the Invisible Prompt
LLM content production factory is starting to resemble Hidden Markov Models
Welcome to Infinite Curiosity, a newsletter that explores the intersection of Artificial Intelligence and Startups. Tech enthusiasts across 200 countries have been reading what I write. Subscribe to this newsletter for free to receive it in your inbox every week:
In the era of Large Language Models (LLMs), the question is no longer whether something was written by AI. It likely was! Instead, we should be asking: What was the original prompt?
When reading LLM-generated text, we only see the output. Not the input that shaped it. This made me think of a Hidden Markov Model (HMM). It’s a probabilistic model where we observe outputs but not the hidden states (inputs and intermediate reasoning). So I wanted to explore this analogy further.
What is a Hidden Markov Model?
Hidden Markov Models have been widely used in fields like speech recognition and bioinformatics. At their core, HMMs consist of:
Hidden States: The underlying sequence we cannot directly observe e.g. phonemes in speech recognition or the LLM prompt and reasoning steps.
Observations: The visible data generated from hidden states e.g. sound waves in speech recognition or LLM-generated text.
Transition Probabilities: The likelihood of moving from one hidden state to another, determining how inputs influence outputs.
An example in speech recognition: We do not hear phonemes directly. Instead we infer them from sound waves based on probabilistic mappings. Similarly with LLMs, we only observe generated text and must infer the underlying prompt.
Mapping HMMs to LLMs
The process of LLM text generation made me think of the HMM structure:
Hidden States: The user’s original prompt, latent embeddings, and model activations during generation.
Observations: The final generated text.
Transition Model: The probability distribution governing token selection based on prior context.
Inference Problem: Given the text, can we reverse-engineer the original prompt?
Noise and variability complicate this problem. Even with the same prompt, LLMs may generate different outputs due to randomness in token sampling e.g. temperature settings). This is similar to observing slightly different emissions from an HMM due to probabilistic state transitions.
Why Does This Matter?
Here are a few reasons:
Deconstructing AI-Generated Content
If we assume content is AI-generated, the real question becomes: What intent or structure guided its creation? Understanding prompts allows us to analyze AI-assisted thought processes rather than debating whether AI was used.
Content Generation Arms Race
Companies are already optimizing prompts to shape LLM outputs for SEO, advertising, and engagement. The ability to infer prompts may help in reverse-engineering content strategies and detecting AI-generated content patterns.
Prompt Injection Attacks
If adversaries can infer prompts, they might reconstruct sensitive queries or manipulate LLM behavior. Understanding the hidden structure of prompts can expose vulnerabilities in AI-driven applications.
The Future of Writing
Better techniques are coming along to extract the "hidden" structure behind AI-generated text. We may see new tools for analyzing authorship, intent, and even reconstructing original human inputs from AI outputs.
LLM Content Production Factory as an HMM
We live in a world where prompts are invisible. AI-generated content functions like a Markov chain, with each output probabilistically linked to a hidden input.
The fundamental question is shifting from "Was this written by AI?" to "What was the prompt?"
What can we unlock by viewing AI text generation through the lens of HMMs? I’m curious to find out.
If you're a founder or an investor who has been thinking about this, I'd love to hear from you.
If you are getting value from this newsletter, consider subscribing for free and sharing it with 1 friend who’s curious about AI: