A leading ML educator on what you need to know about LLMs

Maxime Labonne's blunt take: most people are fine-tuning when they should be prompting. The biggest misconception in LLM work is rushing to customize models when few-shot prompting or RAG would solve the problem cheaper and faster. You have specific use cases, specific data—of course you want to fine-tune. But in practice, you can get away with providing a few examples to the model or retrieving context and including it in the prompt.

Here's when each technique matters. Use RAG for injecting external knowledge dynamically—it's efficient, cost-effective, and keeps you from baking outdated information into a model. Reserve fine-tuning for adapting models to domain-specific knowledge or adjusting tone and style. Combine both when appropriate. The catch: data quality is everything. Curated data beats a random dump any day. Think of it as a textbook—give the LLM what a person would need to fully understand the domain.

Understanding the problem and scoping it well is probably the most important step. People will tell you they want to fine-tune a model on a dataset, but that won't solve their problem. You can do it, but then they're disappointed. LLM engineering is software engineering plus domain expertise: inference optimization, quantization, deployment (cloud and edge), RAG pipelines. The LLMOps discipline expects reusable, scalable workflows.

Optimization techniques matter for production: speculative decoding, quantization, model merging (combining multiple fine-tuned models without retraining). Preference alignment refines how models present information. But none of that matters if you solved the wrong problem with the wrong technique. Try prompt engineering first, then RAG, then fine-tuning only when those don't meet your objectives for quality, cost, or latency.