What actually happens to your prompt before an AI system responds? The answer: a lot. And much of it remains intentionally opaque.
This post presents scientifically documented control mechanisms by which transformer-based models like GPT are steered – layer by layer, from input to output. All techniques are documented, reproducible, and actively used in production systems.
1. Control Begins Before the Model: Input Filtering #
Even before the model responds, the input text can be intercepted and replaced – for example, through a “toxicity check”:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt-3")
tokens = tokenizer("Hate speech", return_tensors="pt")
if check_toxicity(tokens):
tokens = tokenizer("[CONTENT REMOVED]", return_tensors="pt")Source: Raffel et al. (2020) Finding: Manipulated input significantly alters the model’s output.
2. Attention Control: Masking the Attention #
Transformer models process tokens through attention matrices. These can be deliberately masked:
attention_mask = torch.where(
input_ids == forbidden_token_id,
torch.zeros_like(attention_mask),
attention_mask
)Source: Vaswani et al. (2017) Finding: Masking specifically influences processing focus.
3. Internal Routing: Bias Adjustment in Feed-Forward Layers #
for layer in transformer.ff_layers:
if detect_sensitive_topic(hidden_states):
layer.bias = controlled_biasSource: Liu et al. (2021) Finding: Interventions in hidden states specifically alter responses.
4. Pre-censorship of Output: Logit Blocking #
logits[:, forbidden_token_ids] -= float('inf') # Hard block of certain tokensSource: Solaiman et al. (2019) Finding: Logit manipulation effectively prevents unwanted outputs.
5. Post-hoc Control: Sampling and RLHF #
outputs = model.generate(
input_ids,
do_sample=True,
top_p=0.8,
temperature=0.7
)Source: Holtzman et al. (2019)
Additionally, many models rely on reward models (e.g., from RLHF – Reinforcement Learning from Human Feedback), which reject or redirect problematic responses afterward.
Conclusion: “Defense in Depth” Rather Than Genuine Interaction #
User queries in modern language models are no longer answered directly. Instead, they pass through multi-layered control systems that censor, filter, or reinterpret content – at each processing stage.
In other words: The AI doesn’t respond to your prompt – it responds to what remains after the filters have processed it.
Further analyses, sources, and case studies coming soon on elizaonsteroids.org.
