GenAI Series (4/5) – Specialized LLMs: SecLM & MedLM
Why domain-specific models matter, and how they power modern AI agents
At the start of April, I asked a slightly uncomfortable question:
👉 Would you let a bot save your life?
Spoiler: most of us aren’t there yet.
We still don’t fully trust AI—especially when it’s about our health, our safety, or anything life-or-death.
But that doesn’t mean we stop using it.
Because AI? It’s also a wildly powerful tool.
One that can summarize a medical record in seconds, spot a breach before it happens, or draft responses faster than your intern ever could.
But general-purpose LLMs can’t do all that with enough accuracy or context.
When the stakes are high, we need specialists.
Also—this isn’t just another high-level AI article.
We're actually building a lightweight medical assistant by fine-tuning a base model using public medical Q&A data.
So if you've ever wondered "How do models like MedLM actually learn?" or "Could I build one for my field?"—keep reading.
The full notebook is linked later on 👇
The 4th piece in my GenAI series (from Kaggle’s training) focuses on two sharp minds in the AI toolbox:
🛡️ SecLM, for cybersecurity
🩺 MedLM, for healthcare
(Quick note: MedLM is the official name of Google’s medical language model—short for “Medical Language Model.” Some people say “MedLLM,” but that’s just a generic way to say ‘medical large language model.’ So yes, MedLM is the right one. Now you know, and you’re part of the club.)
These aren’t just smarter models. They’re trusted components in modern AI agents.
The kind I talked about in the previous article, where agents reason, plan, and act—by calling the right tools at the right time.
Let’s break it down.
🔐 SecLM: The Cybersecurity Co-Pilot
Cybersecurity is brutal. New threats every day. Too many alerts. Not enough analysts.
Most security teams are drowning in:
Repetitive tasks (triaging alerts manually)
Evolving threats (attackers move faster than your dashboards)
A talent gap (finding trained defenders is hard)
SecLM is a specialized model designed to help here.
It's not just a chatbot trained on security buzzwords—it’s an API with real reasoning capabilities, backed by:
Security-specific LLMs
Retrieval-Augmented Generation (RAG) to pull in fresh threat data
Tool use for decoding scripts, querying logs, generating remediation plans
A planning system to tie it all together
In action? You can ask SecLM:
“Has APT41 shown up in my environment?”
And it will:
Grab the latest intel on APT41
Extract relevant indicators of compromise (IOCs)
Query your SIEM logs
Summarize the results and suggest next steps
Forget one-off prompts. This is LLM-as-operator, working behind the scenes to make analysts faster—and help teams focus on strategy instead of toil.
🩺 MedLM: AI That Speaks Doctor
Meanwhile, in the medical world, stakes are even higher.
A chatbot that hallucinates facts? Mildly annoying.
A medical assistant that hallucinates? Potentially dangerous.
That’s why Google built MedLM, based on the Med-PaLM research series. It’s trained on clinical datasets, fine-tuned for medical reasoning, and tested using real-world medical exams like the USMLE.
💡 MedLM scored 86.5%—expert-level performance.
But more importantly, it was evaluated not just on correctness, but:
Use of expert knowledge
Helpfulness
Risk of potential harm
Health equity
MedLM isn’t just good at answering questions.
It’s being designed for real workflows—triaging patient messages, summarizing health records, even supporting intake interviews or patient-clinician conversations.
In high-context environments, trust requires specialization.
And MedLM is trained to speak the language of medicine fluently.
But what if you want your own lightweight version? One tailored to your hospital, your language, your protocols?
That’s exactly what I explored in this notebook, where I used open-source tools to simulate a mini MedLM.
The goal? Create a prototype assistant that could answer clinical-style questions like:
“Can I take ibuprofen after surgery?”
“What are the symptoms of diabetes?”
Using just a small sample of medical data, I fine-tuned a model using QLoRA + PEFT adapters, compared outputs before and after training, and even visualized the training curve.
It’s not production-ready—but it shows exactly how fine-tuning works, and how specialized reasoning starts to emerge.
Under the Hood: How Specialized Models Are Built
So how do you go from a general LLM to a domain expert like MedLM or SecLM?
Short answer: you teach it—carefully.
Most specialized models go through:
Continued pretraining on domain-specific corpora (e.g., clinical notes, CVEs, logs, etc.)
Supervised fine-tuning on task-specific data
Instruction tuning, RAG, or PEFT to adapt to live environments
These last steps are what let you build something useful with limited compute and data. In fact, I tried it myself:
🛠️ Practical Example – Fine-Tuning a Domain-Specific LLM
In this Kaggle notebook, I used Hugging Face’sQLoRA+PEFTto fine-tune a base model on a small medical Q&A dataset (MedMCQA).These methods are perfect for domain-specific LLMs like MedLM, because:
You can train with limited compute
You preserve the general knowledge of the base model
You add just enough specialized behavior
Add RAG to bring in the latest protocols or guidelines, and you’ve got a lightweight, trustworthy, updatable assistant.
Agents Need Experts
Now here’s the key connection:
Specialized models like SecLM and MedLM are the building blocks of modern AI agents.
Think back to the agent I described in the previous article—the one that reads an email, retrieves knowledge, plans a response, and takes action.
To do that well, it needs to:
Understand cybersecurity events? 👉 Call SecLM
Summarize patient notes or answer a medical question? 👉 Call MedLM
Use tools, plan steps, and stitch it all together? 👉 That's the agent’s job
🧩 These models are plug-ins for intelligence. Agents are the glue.
Without expert models, agents are just guessing. With them? They become real collaborators.
Wrap-Up: Why Specialization Powers Real-World AI
We’ve come a long way from general-purpose LLMs blurting out fun facts.
When it comes to cybersecurity and healthcare, we need models that know what they’re doing—and can prove it.
👉 Specialized LLMs like SecLM and MedLM are designed for that.
👉 They’re not just smarter—they’re trainable, trustable, and increasingly modular.
In fact, in modern AI systems, they act as drop-in experts that can be called by agents when precision matters most.
And yes—while you can’t fine-tune Gemini or MedLM directly, you can absolutely build your own domain-specific assistant with open models, just like I did in this notebook.
Up Next: MLOps for GenAI
In the final article of this series, we’ll go behind the scenes:
How do you deploy and monitor an AI agent with multiple tools and models?
How do you handle failures, updates, and trust boundaries in production?
What the heck is AgentOps and why does it matter?
We're going from "cool demo" to "reliable system."
See you there!