LLM training
The process of training large language models on vast text datasets, which determines the foundational knowledge and brand associations an AI model carries.
LLM training is the process by which large language models learn to understand and generate human language. During training, models process billions of text tokens from web pages, books, academic papers, and other sources — forming the foundational knowledge that shapes how they discuss brands, products, and topics.
Training phases
1. Pre-training
The model learns general language understanding from massive datasets:
- Processes trillions of tokens from diverse text sources
- Learns grammar, facts, reasoning patterns, and world knowledge
- Takes weeks to months on thousands of GPUs
- Results in a "base model" with broad but unrefined capabilities
2. Fine-tuning
The base model is refined for specific behaviors:
- Instruction tuning teaches the model to follow directions
- Safety training reduces harmful outputs
- Domain-specific training improves performance on targeted tasks
3. RLHF / RLAIF
Reinforcement learning from human (or AI) feedback aligns the model with user preferences:
- Human evaluators rate model outputs
- The model learns to produce responses that humans prefer
- This stage shapes how the model presents and recommends brands
How training affects brand visibility
Your brand's representation in training data determines:
- Baseline knowledge: Whether the AI "knows" about your brand at all
- Brand associations: What attributes and qualities the AI connects to your brand
- Competitive positioning: How the AI compares your brand to competitors
- Accuracy: Whether facts about your brand are correct or hallucinated
The training data window
LLMs have a knowledge cutoff — a date beyond which they have no training data. For brands:
- New products launched after the cutoff may not exist in the model's knowledge
- Recent rebranding or repositioning may not be reflected
- This is why real-time RAG and web search are critical — they supplement training knowledge with current information
Influencing future training
While you cannot directly control training data, you can maximize your representation:
- Publish authoritative content that earns wide distribution
- Ensure major publications and trusted sources cover your brand accurately
- Maintain a presence on high-quality websites that are commonly included in training datasets
- Keep your brand information consistent across Wikipedia, industry databases, and review platforms
- Allow AI crawlers access to your content
