In the ever-evolving world of AI, large language models like GPT-4 have earned celebrity status, much of it thanks to user-friendly designs and its ability to mimic human language and generate content across domains. This has sparked everything from boardroom investment frenzies to kitchen-table career shifts.

But there’s a core problem most users — and even some professionals — misunderstand.

LLMs don’t “know” anything.

Any AI pro knows that despite the hype, LLMs aren’t omniscient. They don’t possess a database of facts or access real-time information. Their outputs are generated based on probabilities learned from patterns in the data they were trained on — which is finite, static, and increasingly outdated.

So why do so many still believe that more parameters or more training data equals better answers?

Because it feels that way… until it doesn’t.

Myth #1: “LLMs Know Everything”

One of the most common misconceptions is treating LLMs like AI oracles. People ask them questions expecting definitive, up-to-date answers — but forget one fundamental truth:

LLMs have a knowledge cutoff.

Unless integrated with other systems, LLMs can’t access recent data, current events, or proprietary databases. This means they might confidently give you the wrong answer, without signaling any uncertainty. Worse still, they may fabricate information entirely — a phenomenon known as hallucination.

And for data professionals, hallucinations are more than just a quirky bug. They’re a liability — especially when stakes involve compliance, trust, or operational efficiency.

Myth #2: “More Data = Better Answers”

Another flawed assumption is that scaling a model with more data and computing automatically leads to better performance.

While scale can improve general capabilities, it doesn’t solve fundamental issues:

• LLMs don’t reason in the way humans do.
• They lack context outside their training data.
• Their outputs are not inherently verifiable.

Throwing more data at the problem doesn’t change this — it just makes the hallucinations sound more convincing.

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Enter RAG: Retrieval-Augmented Generation

Here’s where RAG (Retrieval-Augmented Generation) enters the conversation as a game-changer.

Instead of relying solely on pre-trained parameters, RAG integrates LLMs with external knowledge sources (like vector databases, search engines, or structured documentation). When prompted, the model doesn’t guess — it retrieves relevant content and uses it to generate responses grounded in real, referenceable information.

RAG fixes what LLMs alone can’t:

• Recency: Pulls in fresh, up-to-date knowledge.
• Verifiability: Lets users trace outputs back to source materials.
• Domain specificity: Leverages niche or internal datasets.
• Reduced hallucinations: Anchors language generation in reality.

So Why Isn’t Everyone Using RAG?

RAG isn’t magic. It requires thoughtful implementation, good document hygiene, and an understanding of vector search mechanics. But when done right, it transforms LLMs from language models into knowledge engines.

That’s exactly what we dive into during Week 3 of the ODSC AI Bootcamp.

Week 3 of the ODSC East Bootcamp: From Hype to Hands-On with LLMs + RAG

This isn’t just another webinar that leaves you with buzzwords and more questions. Week 3 of the ODSC Spring AI Bootcamp is built for working professionals who want to:

• See where LLMs fall short in real-world workflows
• Understand how RAG works under the hood
• Build hands-on, verifiable GenAI applications
• Avoid the common traps that make outputs unreliable

In short, you will leave with day-one actionable skills – period.

How? Well, that’s by learning from industry veterans with real implementation experience, getting access to code, and walking away with practical frameworks that can be applied in your work right out the door.

ODSC East 2025 will make sure that you don’t just theorize – you’ll build.

LLMs are powerful, but they’re only as effective as the infrastructure around them. If you want to move from hype to reliable performance, RAG is the unlock.

Our AI Bootcamp breaks these myths down with practical, hands-on examples — not slide decks. If you’re a data scientist, ML engineer, or AI-curious technologist looking to upskill fast and effectively, this week is built for you.

Ready to join Week 3 of the AI bootcamp? Secure your spot now and start building LLM solutions you can trust.