The Ten Input Factors
That Determine LLM Output

FACTOR 01
Signal-to-Noise Ratio

The proportion of tokens in the input that are directly relevant to the current task relative to the total token count.

The optimal point is not “minimum input” but rather “just enough highly relevant input.” The optimization direction for signal-to-noise ratio is to increase “signal,” not merely compress “noise.”

FACTOR 02
Logical Structure

The hierarchical relationships, classification schemes, and organizational forms among different parts of the input.

The value of structure depends on task type. For instruction-following tasks, good structure helps enormously; for open-ended retrieval, excessive structure may actually interfere.

FACTOR 03
Positional Effects

How the same piece of information placed at different positions within the input produces different effects on model behavior.

The severity of positional effects is highly dependent on task complexity. In simple factual retrieval it may be negligible, but in complex reasoning and multi-hop QA the impact is enormous. One-size-fits-all conclusions are wrong.

FACTOR 04
Absolute Length Tax

Even when the signal-to-noise ratio is 100%, the absolute token length of the input itself degrades performance.

“Long context = bad” is a false simplification. “Long context carries a hidden tax, and the tax rate varies by scenario” is more accurate. On simple QA, large models are almost unaffected (<2%), but on complex reasoning tasks all models degrade significantly.

FACTOR 05
Semantic Distractor Interference

Topically similar but factually irrelevant content that produces interference beyond simple noise.

FACTOR 06
Instruction Hierarchy Conflict

Priority conflicts between instructions at different levels — system prompts, user messages, tool returns, etc.

FACTOR 07
Model-Specific Behavior

Different models exhibit fundamentally different failure modes under the same input.

FACTOR 08
Output Self-Degradation

When generating long outputs, previously generated text becomes part of the input for subsequent generation, causing progressive quality decline in later segments.

FACTOR 09
Format Bias

Models’ ability to process different data formats (JSON, YAML, Markdown, etc.) varies due to training data distribution, independent of logical structure.

When designing system prompts and tool schemas, prefer JSON format (most abundant in training data). Avoid YAML for critical outputs. Note that serialization library field ordering may affect output quality.

FACTOR 10
Inference-Time Configuration

Temperature, top-p, reasoning effort, and other inference-time parameters are strictly part of the “input” but are rarely included in prompt engineering discussions.

Inference-time configuration is often the lowest-cost optimization lever — zero token cost, yet it can significantly affect output determinism and consistency.

Semantic distractors × instruction conflict / Model-specific behavior × output degradation / Precise mathematical relationship between SNR × logical structure / Format bias × positional effects / Inference-time config × any of the above — all remain current research gaps.

Agent architecture yields net benefits when: (a) the task naturally decomposes into independent sub-steps, (b) each step has a clear success/failure signal (e.g., code compiles), (c) intermediate results can be verified. It yields net costs when: (a) the task requires global coherence (e.g., long-document writing), (b) intermediate steps lack verification mechanisms, (c) system prompt re-transmission overhead is unacceptable in cost-sensitive scenarios.

When using GPT-5.4 for simple translation or template filling, the model will almost always succeed regardless of noisy, poorly structured, poorly positioned input. The ten factors primarily matter for tasks “near the boundary of model capability.”

Code has compilers and unit tests; data extraction has schema validation. When incorrect output can be automatically detected and retried, single-pass accuracy requirements drop, and the marginal value of input optimization decreases accordingly.

Creative writing, brainstorming, and style exploration have no single “correct answer.” The metric of “accuracy” itself does not apply, weakening the influence of most factors. However, instruction hierarchy and logical structure remain important — they affect “whether user intent is followed” rather than “whether facts are correct.”

When user and model iterate rapidly (e.g., editing in Cursor), each interaction handles only a small incremental change. Input absolute length and positional effects are naturally compressed. Response latency may matter more than output quality.

The effect size of the ten factors is not a fixed constant but a function of task difficulty × verification mechanisms × iteration speed. In scenarios with simple tasks, ample verification, and rapid iteration, heavy input optimization is over-engineering. In scenarios with complex tasks, no verification, and one-shot output, input optimization is the deciding factor between success and failure.

The model is the CPU; the input is the program. The CPU keeps upgrading (though more slowly), while our ability to write programs is evolving at breakneck speed.

Signal-to-noise ratio and absolute length are the two factors most worth prioritizing among the ten. Rationale: (a) both report relatively large effect sizes in their respective experimental conditions, (b) EMNLP 2025 demonstrates they stack independently, (c) both are the most prominent pain points in current Agent architectures. However, on simple QA tasks, large models display extreme resilience to both factors (↓<2%), at which point other factors may warrant more attention.

Different products represent different Input engineering philosophies. There is no “optimal,” only “best fit for the scenario.” Independent research comparing input efficiency of all products on the same standardized task does not yet exist.

“Less input” does not equal “better input.” The optimal point is “just enough highly relevant input.” Multiple counter-studies show that in information-scarce scenarios, adding input (few-shot examples, more retrieved documents) substantially boosts performance.

Context engineering is evolving from a subset of prompt engineering into an independent engineering discipline. Yet pre-training advances (the GPT-4→GPT-5 series leap) remain a critical source of capability improvement and should not be dismissed in the emphasis on input engineering.