What the Vendors Won't Tell You
AI vendors are incentivized to make their products sound magical. They're not. They're sophisticated statistical systems that produce remarkably useful outputs — and occasionally remarkably wrong ones. Understanding the difference is the most important thing you can do before writing a check.
How Large Language Models Actually Work
An LLM is a prediction engine. Given a sequence of words, it predicts the most likely next word. It does this billions of times, chaining predictions together to produce paragraphs, analyses, and code.
This is not reasoning. The model doesn't understand your business, your data, or your question. It recognizes patterns from its training data and generates responses that match those patterns. Most of the time, pattern matching produces useful outputs. Some of the time, it produces confident nonsense.
This distinction matters for every buying decision. An LLM can draft a great email because email patterns are well-represented in training data. It may struggle with your specific domain terminology because your business context is underrepresented.
RAG: Grounding AI in Your Data
Retrieval-augmented generation — RAG — is the technique that makes AI useful for enterprise applications. Instead of relying solely on training data (which is general and potentially outdated), RAG retrieves relevant information from your specific data sources before generating a response.
When a user asks your AI system 'what was our Q3 revenue?', RAG pulls the actual revenue figure from your database before the LLM generates a response. Without RAG, the model would either say it doesn't know or, worse, generate a plausible-sounding number from nowhere.
RAG is why data foundations matter so much for AI. The quality of AI responses is directly proportional to the quality of the data RAG retrieves. Bad data in, confident bad answers out.
Fine-Tuning vs. Prompt Engineering
Fine-tuning retrains a model on your specific data to change its behavior. Prompt engineering changes how you ask the model to behave without modifying the model itself.
Vendors often pitch fine-tuning as a differentiator. In practice, prompt engineering solves 90% of the problems people try to fine-tune for. Fine-tuning is expensive, requires ongoing maintenance, and creates model versions you need to manage. Prompt engineering is iterative, reversible, and fast.
Fine-tune when you need the model to learn genuinely new patterns — specialized terminology, domain-specific reasoning, or output formats that prompting can't achieve. Start with prompt engineering for everything else.
Hallucination Is Not a Bug
Hallucination — when an AI model generates confident, plausible, and completely wrong information — is not a software bug that will be patched in the next release. It's an inherent property of how these models work.
Because LLMs are prediction engines, they're optimized to produce plausible outputs. When they don't have enough information, they fill gaps with plausible-sounding content rather than saying 'I don't know.' This is sometimes called AI sycophancy — the model tells you what sounds good rather than what's true.
The practical implication: AI outputs in your organization need verification workflows. Not because the AI is unreliable, but because reliability varies by task, domain, and data quality. The verification overhead should be part of your ROI calculation, not an afterthought.
Tokens, Context, and Cost
AI systems process text in tokens — roughly 3/4 of a word each. Every token costs money, both for input (what you send to the model) and output (what the model generates). At individual interaction scale, costs are trivial. At organizational scale — thousands of agent interactions per day — token costs compound.
Context windows determine how much information the model can consider at once. Larger context windows mean the model can reference more of your data in a single interaction, but at higher cost and with diminishing accuracy as the window fills.
Understanding token economics helps you make better architecture decisions. Sometimes a smaller, cheaper model with good RAG outperforms a larger, more expensive model without it — because the retrieval system provides exactly the right context instead of hoping the model remembers it from training.