What They Don’t Tell You About Building with LLMs

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

You’ve seen the threads:

“Just use RAG.”

“Fine-tune it.”

“Build an agent.”

They make it sound simple. But if you’ve actually tried working with LLMs — whether it’s building a product, adding AI into a workflow, or just figuring out where to start — you know how quickly that advice becomes overwhelming.

There’s no shortage of tutorials, blog posts, or Twitter threads. But there’s almost no structured clarity.

• What kind of architecture do I need?
• Which model should I use?
• Do I really need fine-tuning?
• What even counts as a “production-ready” system?

That’s where our 10-Hour Video Primer: Building and Operating LLMs comes in.

A course for people who don’t want to waste time

We’ve spent the last two years building LLM courses, developing internal AI tutors, helping consultancies deploy AI into their work, and supporting one of the largest independent AI learning communities in the world. What we’ve learned is simple:

• Everyone knows they need to use LLMs.
• Very few understand what that actually means.
• Even fewer have a framework for deciding what to build, when, and how.

Our longer programs span 60+ hours and go deep into building full-stack AI systems. But most people don’t need that depth right away. What they need first is a clear map of the terrain — a way to understand what matters, what doesn’t, and where they personally should begin.

This 10-hour video course gives you exactly that!

Get the course for $199 (launch price — going up soon)

Exactly what it promises — a fast‑paced, no‑fluff immersion that cuts through the hype and shows you how large‑language models actually work and where they belong in real products and workflows…What I valued most was the practical toolkit: prompt‑engineering best practices, RAG versus long‑context trade‑offs, vector database basics, fine‑tuning essentials, security and privacy[…]. — Maddie Chalamangalam Sreeramulugari

What you’ll learn:

In five 2-hour video sessions, you’ll:

• Break down the transformer architecture — grasp attention mechanisms, context windows, and tokenization — and learn how these choices impact performance and model selection (open-source vs. proprietary).
• Map out full-stack LLM pipelines, including advanced prompt engineering, RAG with vector databases, and supervised fine-tuning on task-specific data, so you can evaluate and prototype intelligently.
• Evaluate what actually matters, combining automated metrics (BLEU, ROUGE, perplexity) with human-in-the-loop reviews and domain-informed test cases.
• Dissect agent workflows and tool use, understanding when multi-step orchestration is warranted, how tools integrate via API calls or plugins, and how to keep costs and latency under control.
• Master core optimization and safety principles, including model distillation, quantization, RLHF, and prompt injection mitigation — so your systems stay lightweight, safe, and production-ready.

Although the course has a “for developers” focus, it’s relevant to everyone! No prior knowledge is required. A foundation in Python helps but isn’t essential to benefit from the training.

Before you become an expert, become someone who knows what to try first.

✅ 100% money-back guarantee within 30 days if you don’t feel more confident building with LLMs after the course.

👉 Start risk-free

You think you know LLMs. But do you know this?

We designed the curriculum around the most common gaps we’ve seen — even among intermediate practitioners:

1. Let’s say you know how to prompt, choose a model, maybe even fine-tune. But do you really know what’s happening under the hood?

You’ll gain a working knowledge of transformers — from tokenization to attention to context windows — so you know their real capabilities and limits, not just what the wrapper API abstracts away.

2. Let’s say you’ve tried RAG or fine-tuning — but do you know when to use which, or whether you need either at all?

We’ll break down the tradeoffs, workflows, and edge cases — so you understand when RAG is enough, when to fine-tune, and when neither is worth the effort.

3. Let’s say you’ve seen agents and tool use demos. But do you know how to design one that actually works for your use case?

You’ll explore MCP, A2A, and agent orchestration — not as hype, but as concrete architectures that can be evaluated, built, and deployed.

4. Let’s say you know LLMs hallucinate. But do you know their deeper flaws — latency, context overflow, brittleness?

You’ll learn to recognize the real limitations of large models and apply mitigation strategies that go beyond “add more grounding.”

5. Let’s say you’ve run some evals. But do you know what makes a good one?

We’ll cover evaluation techniques that matter in production — model-based feedback, human-in-the-loop setups, and custom domain metrics.

6. Let’s say your prototype looks good on paper. But is it usable in production?

You’ll get an intro to distillation, quantization, RLHF, and more — so you know how to make your systems lighter, cheaper, safer, and better at scale.

7. Let’s say you know the tools. But do you know how to think?

You’ll walk away with a practical framework for approaching any LLM project — knowing what to explore, what to avoid, and how to move fast without breaking everything.

Who is this course for?

This course is specifically designed as a 1 day Bootcamp for Software Professionals (language agnostic). Although the course has a “for developers” focus, it’s relevant to everyone! No prior knowledge is required. A foundation in Python helps, but isn’t essential to benefit from the training.

We teach the core LLM skills and techniques together with practical tips. This will prepare you to either use LLMs via natural language or explore documentation for LLM model platforms and frameworks in the programming language of your choice, and start developing your own customized LLM projects.

The course brilliantly cuts through the overwhelming flood of information on LLMs, delivering knowledge in digestible, bite-sized modules that build confidence step by step. The sections on prompt engineering and API integration were particularly valuable, providing practical skills I immediately applied to create custom AI tutors for my own learning projects. — Matt Chantry

What else do you get?

• 📼 Self-paced video lectures — All sessions are pre-recorded, so you can learn on your schedule.
• 🔁 Lifetime access — Includes all future updates.
• 🧑‍🏫 Live Kick Off Call + Private Discord — Ask questions, get feedback, connect with instructors.
• 📜 Certificate of completion — Showcase your skill on LinkedIn or your resume.
• 💵 100% money-back guarantee within 30 days — If it doesn’t help you build better with LLMs, get a full refund.

Launch Price: $199

This course was made to cut through that noise. We typically charge companies $25K–$50K to guide these decisions. Now, for a limited time, you get that same clarity for just $199.

And if you join now, you’ll also get free access to the new 2-hour module on fine-tuning open models — coming soon. After that, the price will increase.

If you’ve been circling LLMs but not sure where to go next, this is your moment to move with focus.

👉 Join the 10-Hour Primer now

Build smarter. Start sooner. Know what actually matters.

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Published via Towards AI