Prompting Wasn’t Enough: How I Went Too Far With LLMs

Hi, I’m James, and I’m an LLM-aholic. This is my story.

Stage 1: From Casual Curiosity to Everyday AI Assistant

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.

Stage 2: Becoming a Prompt Engineer

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.

Stage 3: LLM-Powered Knowledge Retrieval with RAG

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.

Stage 4: Function Calling & Real-World Automation

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.

Stage 5: LLM Personalization with PEFT (Parameter-Efficient Fine-Tuning)

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.

Stage 6: The Risks of Full LLM Fine-Tuning

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.

Stage 7: The Final Step: Building a Custom LLM

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.

Recovery Mode: Lessons from an LLM Overload

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

Final Thoughts: Start Simple, Scale with Purpose

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.