How to train an LLM (as a Prompt Engineer, not a soccer coach)
So you’ve mastered prompt engineering—kind of. You know how to write instructions that make a large language model sound like a polite customer service rep or, depending on your business needs, a swashbuckling pirate with strong opinions about “The Goonies”. But sometimes… prompts alone aren’t enough. You need to train the thing.
Welcome to the next level.
Training a large language model (LLM) doesn’t mean you’re building GPT-5 from scratch (unless you’re independently wealthy, in which case—adopt me?). It usually means fine-tuning an existing model to better suit your product, tone, domain, or chaos containment strategy.
Here’s what prompt engineers need to know about training LLMs.
🤖 What is LLM training, anyway?
Large Language Model (LLM) training comes in two main flavors: pre-training (the big foundational stuff that happens on hundreds of GPUs across the universe) and fine-tuning (where you, the humble prompt engineer, teach a model to act a bit more like your model).
In most cases, you're not doing the full-scale pre-training—that’s OpenAI, Anthropic, Google’s job. Instead, you're fine-tuning: adjusting an existing model’s behavior by training it on your data—so it speaks your language, understands your workflows, or just stops hallucinating bullshit.
It’s like onboarding a new employee. The base model knows English and can write emails—but it doesn’t know how your particular company writes emails. That’s where YOU come in.
🧠 Understand when you actually need to train
Rule of thumb: If you can’t fix the behavior with better prompting, better context, or retrieval augmentation (RAG)… it might be time to train.
What this looks like in a job environment: Your company is rolling out a chatbot, and despite your best prompting efforts, it still gives awkward, generic responses. Leadership wants a brand voice that actually sounds human. You're asked to gather real customer service chats and explore fine-tuning the model.
Rule of thumb: If you can’t fix the behavior with better prompting, better context, or retrieval augmentation (RAG)… it might be time to train.
Common reasons to fine-tune:
You want the model to speak in your brand voice 100% of the time
You’re working with jargon-heavy, proprietary knowledge
You need faster responses without relying on large context windows
Things you don’t need training for:
The model keeps saying “As an AI language model…” → prompt fix
You need to cite documents → RAG
Your boss is bored and wants to sound smart → vibes
📦 Get your data together (and clean it!)
What this looks like in a job environment: You’re handed a chaotic Google Drive folder full of email threads, Slack screenshots, and weird CSVs labeled "final_FINAL2." Your job is to turn this mess into a clean, labeled training set that won’t confuse the model or get your team roasted in the next demo. Before you train, you need a dataset. This might be:
Datasets: Before you train, you need a dataset. This might be:
Chat logs
Support tickets
Legal documents
User reviews
Anything you want the model to learn to mimic
Format matters. For most fine-tuning tasks, you’ll want JSONL format like this:
{"prompt": "How do I reset my password?", "completion": "To reset your password, click on 'Forgot Password' at the login screen..."}
Pro tips:
Remove duplicates, weird formatting, or irrelevant noise
Keep completions short and clean
Avoid training on already-generated model responses unless they’re excellent
⚙️ Choose your model & method
What this looks like in a job environment: Your team wants a lightweight assistant that works offline, so you recommend using a smaller open-source model and training it with QLoRA. Your PM asks if it’ll still work in the app. You say yes. You Google for two hours to confirm. You’ve got options:
a. OpenAI Fine-Tuning
Works with smaller models (e.g., GPT-3.5 Turbo)
Easy-to-use CLI
Great docs: OpenAI Fine-Tuning Guide
b. Hugging Face Fine-Tuning
More control, more mess
Use models like LLaMA, Falcon, Mistral, etc.
Tutorials galore: Hugging Face Course
c. Parameter-Efficient Fine-Tuning (PEFT)
Use LoRA, QLoRA, etc.
Lighter on resources
Ideal for startups or solo hackers
🧪 Test like a mad scientist
What this looks like in a job environment: After fine-tuning, the chatbot suddenly thinks it's your CEO. You spend the day running outputs through your evaluation checklist, frantically Slack-ing the ML lead, and A/B testing to figure out if the base model actually performed better. (It did.)
Train → Evaluate → Refine → Repeat.
Create a test set with examples you didn’t train on.
Evaluate:
Accuracy
Tone
Completeness
Weirdness levels (technical term)
Try:
A/B testing trained model vs. base model
Human evals if you’ve got a team
Using tools like Trulens, LangSmith, or plain ol’ spreadsheets
🛠️ Deploy (carefully) and monitor
What this looks like in a job environment: Your fine-tuned model goes live in the product. Two days later, support gets a ticket saying the bot recommended a competitor’s website. You add a filter to the outputs, write a fallback prompt, and gently pretend that was part of your plan all along. Fine-tuned models should still have guardrails. Even your beautiful trained LLM can go rogue.
Add:
Moderation layers
Fallback prompts
Logging + observability (watch those tokens, baby!)
Monitor usage and retrain as needed. The internet evolves fast, and so does language.
🌟 Bonus: Your role as the human in the loop
What this looks like in a job environment: You’re in a team meeting and someone says, "The model’s acting weird again." Everyone turns to look at you. You sigh, open your laptop, and start comparing outputs with the calm resignation of someone who chose this life. Even after training, your job isn’t done. You’re now:
You’re now:
The overseer of tone
The first responder when the model loses its mind
The person saying, “Okay but what if we trained it… again?”
You are not just a prompt engineer—you’re an AI behavior shaper. The robot wrangler. The model whisperer.
Final thoughts
Fine-tuning isn’t scary. It’s just another tool in your increasingly absurd AI toolkit. Whether you’re cleaning data while caffeinated at midnight or yelling "WHY IS IT RECOMMENDING SOURDOUGH STARTERS AGAIN," just know—you’re doing great.
So go forth. Train the thing. And when it breaks, you’ll be right there with your prompts, your patches, and your pastel Post-its.
