A CTO's Practical Guide to LLMs

Written by Georgije Stanisic on October 10, 2024

In almost every conversation I have with other business leaders, the same question comes up: “How should we be using AI?” The arrival of Large Language Models (LLMs) has been nothing short of transformative. They offer a glimpse into a future of incredible efficiency. But as a CTO, I know that with any powerful new technology, the initial excitement must be balanced with a clear-eyed assessment of the risks.

My goal here isn’t to sell you on the hype. It’s to share my perspective on how to think about these tools, where they excel, where they fail, and most importantly, how to deploy them in a way that is both powerful and safe for your business.

The Brilliance and Blind Spots of a New Hire

Let’s start with the magic. What makes these models so powerful is their incredible fluency. Having been trained on a staggering amount of text from the internet, they have an innate grasp of language, tone, and context. They can generate marketing copy, draft emails, and answer questions with a speed that is simply superhuman.

A mental model I’ve found incredibly useful is to think of an LLM as a brilliant, brand-new English Literature graduate you’ve just hired.

This new hire is a phenomenal writer. Their command of the English language is second to none. The problem? They’ve never worked a day in our industry, let alone at our company. They have zero knowledge of our brand voice, our internal compliance policies, our customers’ pain points, or our product specifications. They are a blank slate when it comes to the things that actually matter to our business.

Let’s put this graduate to a real-world test. Imagine you ask them to “write a blog post about our new software.” They’ll likely produce something that’s well-written but completely generic. To fill the gaps in their knowledge, they might even invent features or benefits that sound plausible but are entirely false.

Now, imagine we try again. This time, we give them a product one-pager, two customer case studies, and our internal style guide. The results are worlds better. The post is specific, on-brand, and factually grounded. But we’re not out of the woods yet. The graduate might still misinterpret a key detail, over-emphasize a minor point, or make a “creative leap” that, while well-intentioned, completely misses the mark. They have the right documents, but they still lack the experience to use them perfectly.

When “Creative Leaps” Become Catastrophic Risks

This is the part of the conversation that I believe is most critical for any business leader. An LLM’s tendency to confidently “make things up” is technically called a hallucination, and it represents a significant business risk. The severity of that risk depends entirely on the context.

• Low-Stakes: Let’s say we’re using an LLM to help our marketing team brainstorm email drafts. If the model produces something a little off-brand, the team will easily catch and correct it before it ever goes out. The risk is negligible.
• High-Stakes: Now, imagine we’re using an LLM as an internal assistant for our legal team to find precedents. If the model hallucinates and cites a non-existent case law, the consequences could be disastrous, leading to immense financial and legal liability.

My advice is this: for any application where a wrong answer has real consequences for your customers, your employees, or your bottom line, an out-of-the-box LLM is a gamble you simply can’t afford. Yes, even the best human employee can make a mistake. The difference is that a human employee doesn’t confidently invent facts from thin air; they might get something wrong, but the nature of an LLM’s error is fundamentally different and more dangerous.

Our Solution: The Expert in the Locked Room

So how do we get the best of both worlds? How do we harness the LLM’s incredible fluency without exposing ourselves to its dangerous unreliability?

Let’s go back to our English Lit graduate. Imagine we take them and place them in a locked room. Inside that room, they have access to one thing and one thing only: our company’s complete, approved, and verified library of information. This includes all our product documentation, our internal knowledge base, our HR policies—everything that defines our ground truth. We then give them one simple rule: you can only use the documents in this room to answer questions or create content. No internet. No prior knowledge.

This is precisely the strategy we’ve built our company, ConversifAI, around. It’s a technology called Retrieval-Augmented Generation (RAG).

RAG acts as the “locked room” for the LLM. It connects the model directly to your private, curated data. When a user asks a question, the RAG system first retrieves the relevant, factual information from your documents. Only then does it hand those verified facts to the LLM with an instruction: “Generate an answer using only this information.”

This process transforms the LLM from a brilliant-but-unreliable generalist into a true, verifiable expert on your business.

Putting a Trusted AI to Work for You

To sum it up, LLMs offer phenomenal speed and fluency. But on their own, they are naive tools that carry an unacceptable risk of hallucination for most serious business applications. The key to deploying them safely and effectively is to ground them in the ground truth of your own data.

At ConversifAI, we don’t just sell technology; we provide a framework for trust, ensuring your AI agents are both intelligent and reliable.

If this is a challenge you’re facing, I’d personally invite you to connect with us. Let’s talk about how you can build a custom AI expert that truly understands your business.

Want to explore how a secure, accurate, RAG-powered chatbot can benefit your organization? Get in touch with Conversifai today!