It started innocently.
“Tell me a joke, ChatGPT.”
“What should I make for dinner, Claude?”
“Gemini, what’s the meaning of life?”
At first, large language models (LLMs) were just fun. I used them like most people do: as digital assistants for quick answers, recipes, and the occasional laugh. But soon, I stopped reaching for Google and started depending on LLMs for productivity tasks.
Rewriting Slack messages. Polishing emails. Summarizing long PDFs. Brainstorming birthday gifts. Drafting LinkedIn posts. My workflow became faster, cleaner—unmatched.
It felt like magic.
That’s when I discovered prompt engineering—a skillset that lets you guide an LLM to generate better, more useful output.
I learned the power of:
Few-shot prompting
Structured outputs like JSON
Task-specific templates
Suddenly, I wasn’t just using the model—I was shaping it.
Examples like:
“Write a confident, empathetic kickoff email.”
“Explain cloud migration to a CEO.”
“Summarize this contract and highlight legal risks.”
I was no longer prompting.
I was programming.
Prompting had its limits. I didn’t want general advice—I wanted company-specific answers.
That’s where Retrieval-Augmented Generation (RAG) came in.
RAG lets you extend LLMs with custom data—like employee handbooks, project docs, or internal wikis—without retraining the model. I used:
Embeddings to represent content
A vector database to store searchable chunks
A system that retrieved relevant info and injected it into prompts
Now the LLM could answer:
“What’s our PTO policy?”
“Summarize the delivery team’s Q3 OKRs.”
“What are our core sales principles?”
It wasn’t just chat.
It was a 24/7 knowledge worker.
Next came tools.
With function calling, I wired the model to act, not just respond. It could invoke APIs to:
Schedule meetings
Pull CRM data
Trigger incident alerts
The LLM became an orchestrator—initiating workflows based on intent.
And I wasn’t coding automation anymore.
I was teaching automation.
Something was still off.
The model worked—but it didn’t sound like us.
Enter PEFT using LoRA (Low-Rank Adaptation): a lightweight fine-tuning method that lets you layer your company’s tone, language, and style on top of a base model.
I fine-tuned the model with:
Sales playbooks
Slack threads
Proposal templates
Soon, it spoke our language:
“Let’s align on the outcome before we talk about the tech stack.”
“We build velocity by focusing on what matters to the business.”
“Don’t worry—we’re outcome-obsessed.”
It didn’t just help.
It represented us.
We wanted more control—so we went for full fine-tuning.
That meant:
Training open-source LLMs on proprietary data
Spinning up GPU clusters
Hiring ML engineers
And it worked… until it didn’t.
The model forgot basic facts—like “Who’s the CEO of Apple?”
That’s catastrophic forgetting: the risk of overwriting general knowledge with narrow fine-tuned data.
We gained control.
But we lost reliability.
And then came the inevitable:
“What if we trained one from scratch?”
We bought tokens.
Spun up multi-node training runs.
Hired researchers.
Built eval frameworks.
Filtered toxic data.
We weren’t customizing anymore.
We were creating.
And the stakes? Everything the model said, implied, or misunderstood—was now ours to own.
Okay, I didn’t build a foundation model (not yet). But I’ve gone far enough to recognize the signs.
Here’s what I’ve learned:
You don’t need your own model.
You probably don’t need fine-tuning.
You can do a lot with:
Smart prompting
Retrieval (RAG)
A few well-placed tools
LLMs are powerful. They’ll boost your productivity, accelerate delivery, and unlock creativity.
But the real challenge isn’t how far you can go—it’s how much value you can unlock before you lose your way.