The AI Vocabulary Layer: LLMs, RAG, and Fine-tuning
In Silicon Valley, AI isn't just a buzzword-it's a new stack. To communicate effectively with engineers and product leaders, you need to move beyond "AI" and understand the specific components of modern generative systems.
The Core Engine: LLMs
A Large Language Model (LLM) is a type of AI trained on vast amounts of text. It predicts the next word in a sequence.
- Key phrase: "We are using a base model (like GPT-4 or Llama 3) and adding our own logic on top."
RAG: The Library
Retrieval-Augmented Generation (RAG) is the most common way companies use AI today. Instead of the model "knowing" everything, it "looks up" information in a database before answering.
- Analogy: If the LLM is a smart student, RAG is giving that student an open-book exam with your company's manual.
- Key phrase: "Our RAG pipeline ensures the model doesn't hallucinate (make things up) about our pricing."
Fine-tuning: The Specialist
Fine-tuning involves taking a pre-trained model and training it further on a smaller, specific dataset.
- Use case: Teaching a model to write code in a very specific, internal company style.
- Key phrase: "We fine-tuned the model on our support tickets to better match our brand voice."
Inference: The Output
Inference is the act of using the model.
- Key phrase: "We need to optimize our inference latency because the chatbot is taking too long to reply."
Summary Table
| Term | What it is | Business Value | | --------------- | ------------------------------ | ------------------------ | | LLM | The core reasoning engine | General intelligence | | RAG | Connecting the engine to data | Accuracy & Facts | | Fine-tuning | Customizing the engine's style | Brand & Domain expertise | | Inference | Running the engine | User experience |
Understanding these distinctions allows you to discuss AI strategy without sounding like a tourist.