Inference

Inference is the production phase of AI. While training teaches a model to understand patterns and relationships, inference is where the model applies that learned knowledge to generate real-world outputs. Every time you ask ChatGPT a question, request a summary from Perplexity, or see an AI Overview in Google search results, you are witnessing inference in action.

How Inference Works

The Inference Pipeline

When a user submits a query to an AI system, the following process unfolds in milliseconds.

1. Input processing - The user’s text is tokenized and converted into numerical representations
2. Forward pass - The tokens pass through the model’s neural network layers
3. Output generation - The model produces probability distributions over possible next tokens
4. Token selection - A decoding strategy selects the next token based on those probabilities
5. Iteration - Steps 2-4 repeat until the response is complete

Autoregressive Generation

Modern LLMs generate text one token at a time. Each new token is appended to the sequence and fed back into the model to generate the next token. This autoregressive process is why you sometimes see AI responses appearing word by word.

Query: "What is AEO?"

Step 1: Model predicts → "AEO"
Step 2: Model predicts → "stands"
Step 3: Model predicts → "for"
Step 4: Model predicts → "Answer"
Step 5: Model predicts → "Engine"
Step 6: Model predicts → "Optimization"
...continues until complete

Decoding Strategies

The way tokens are selected during inference significantly affects the quality and character of the output.

Strategy	Description	Use Case
Greedy Decoding	Always selects the highest-probability token	Fast, deterministic, but less creative
Beam Search	Explores multiple candidate sequences in parallel	Translation, structured outputs
Top-k Sampling	Randomly samples from the k most likely tokens	Creative text generation
Top-p (Nucleus) Sampling	Samples from tokens covering p cumulative probability	Balanced quality and diversity
Temperature Scaling	Adjusts the probability distribution sharpness	Controls randomness

Temperature and Its Effects

Temperature	Behavior	Best For
0.0	Deterministic, always picks the top token	Factual responses, code
0.3-0.5	Mostly deterministic with slight variation	Search answers, summaries
0.7-1.0	Balanced creativity and coherence	General conversation
1.0+	Highly creative, less predictable	Brainstorming, fiction

AI answer engines typically use low temperature settings to ensure factual, consistent responses.

Inference Performance and Optimization

Key Performance Metrics

• Latency - Time to generate the first token (time-to-first-token, or TTFT)
• Throughput - Tokens generated per second
• Cost per token - Computational expense of each generated token
• Memory usage - GPU memory required to hold the model during inference

Optimization Techniques

Technique	How It Works	Trade-off
Quantization	Reduces precision of model weights (e.g., 32-bit to 8-bit)	Slight quality loss for major speed gain
KV-Cache	Caches key-value pairs to avoid redundant computation	Uses more memory, but speeds up generation
Speculative Decoding	Uses a small model to draft tokens, verified by the large model	Faster throughput
Batching	Processes multiple requests simultaneously	Higher throughput, slightly higher latency
Distillation	Trains a smaller model to mimic the larger one	Smaller, faster model with some quality loss

Inference in AI Answer Engines

The Full Answer Engine Pipeline

When an AI answer engine processes your query, inference is just one stage in a larger pipeline.

1. Query understanding - The model (via inference) interprets what you are asking
2. Retrieval - A search system finds relevant sources
3. Ranking - An inference step scores and ranks the retrieved sources
4. Generation - The main inference step produces the answer using retrieved context
5. Citation - The model attributes information to specific sources
6. Safety check - A final inference step reviews the response for quality and safety

Scale of Inference Operations

Major AI platforms handle staggering inference volumes. Each query triggers multiple inference calls across different models and stages. The infrastructure required to serve billions of inference requests efficiently is one of the primary technical challenges in the AI industry.

Inference Costs and the Economics of AI

Inference is the dominant ongoing cost of operating AI systems. While training is a large upfront investment, inference costs scale with every user query.

Cost Factors

• Model size - Larger models cost more per inference
• Sequence length - Longer inputs and outputs require more computation
• Hardware - GPU type and availability affect per-token costs
• Optimization level - Well-optimized inference pipelines reduce cost significantly

This economic reality influences which content AI engines choose to process and cite. Systems are incentivized to retrieve the most relevant content efficiently, favoring concise, high-quality sources that reduce the total inference cost of generating a good answer.

Why It Matters for AEO

Every AI-generated answer that references your content is produced through inference. Understanding this process reveals why certain content characteristics are favored by AI systems. Clear, well-structured content is easier for models to process during inference, producing more accurate and confident outputs. Dense, authoritative content reduces the number of sources the model needs to synthesize, making it a more efficient choice for citation.

For AEO practitioners, the inference process underscores the importance of content clarity and information density. If your content allows the model to generate a high-quality answer with minimal ambiguity, AI systems will preferentially select and cite it. Content that creates confusion or requires extensive reasoning to extract useful information is less likely to be featured.

Genrank tracks how AI answer engines cite and reference your content during inference, giving you actionable data on how to improve your visibility in AI-generated responses.