Free Cheat Sheet from Our 10-Hour LLM Primer

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

How to Really Build on Top of LLMs

Everyone starts with prompts. But if you’ve ever built beyond a toy project, you’ve probably hit this wall:

The model sounds fluent, but the answers are off.

The output looks good, until it breaks your parser.

The demo works, but doesn’t scale.

The fix isn’t always fine-tuning.

In fact, it’s almost never where you should start.

That’s why we made Session 2 of our 10-hour LLM Video Primer free to watch. It covers the actual sequence teams follow to build reliable, cost-aware, and scalable LLM systems, without overengineering or overpaying.

Watch the full session here

But if you don’t have two hours right now, here’s the cheat sheet:

The Four Levels of LLM Customization

1. Prompting: Fastest, cheapest, and surprisingly powerful

Use this when you’re exploring what the model already knows or trying to shape behavior without touching training.

• Use for: general tasks, fast prototyping
• Techniques: zero-shot, few-shot, instruction-based prompting
• Limits: No domain grounding, inconsistent structure, format instability

2. Retrieval-Augmented Generation (RAG): Ground your model in real data

RAG injects external knowledge into your LLM without retraining.

• Use for: knowledge gaps, fast updates, domain-specific context
• Tools: LangChain, LlamaIndex, FAISS, Pinecone
• Pro tip: Improve it with reranking, metadata, query rewriting, and chunking

3. Structured Outputs: Make results predictable

LLMs are flexible, often too flexible. If you’re piping results into another system, structure matters.

• Use for: integrations, automation, safety
• Methods: JSON/XML outputs, schema-constrained prompting, grammar-based decoding
• Tools: Outlines (for decoding), Pydantic (Python), Zod (JS/TS)

4. Fine-Tuning: When you need full control

This is where you adapt the model to your tone, tasks, or workflows, but only if the earlier steps fall short.

• Use for: niche tasks, new domains, tone/style control
• Methods: LoRA, QLoRA, RLHF, DPO, SFT
• Cost: Requires clean data, training infrastructure, and clear ROI

Bonus: The Hidden Infrastructure

Customization is only half the story. Once you’re building seriously, you’ll need to consider:

• Structured evaluation: BLEU, ROUGE, human feedback loops
• Cost & latency optimization: Context caching, quantization, distillation
• Tool orchestration: Agents, APIs, fallback chains
• Model selection: Use the right model for the right job (don’t always default to GPT-4)

These are the kinds of details that make or break real-world AI systems, and they’re exactly what we focus on in the rest of our 10-hour video course.

If this kind of structured, outcome-driven approach clicks with you, we’ve built out the complete system for:

• Designing and deploying full-stack LLM applications
• Running evaluations that actually guide iteration
• Fine-tuning open models with lightweight, GPU-efficient techniques
• Architecting agents, orchestrators, and multi-modal workflows
• Making LLMs safe, fast, and cost-effective in production

Includes lifetime access + all future updates

New modules are in the works, including a bonus session as the landscape evolves.

Current price: $199

No fluff. No hype. Just the path from “exploring” to actually building LLM-powered products that hold up in the real world.

Upgrade to the full course or watch lesson 2 here!

And if you missed Lesson 1, it’s free too.

Together, they’ll give you a grounded understanding of how LLMs reason, retrieve, and fail and how to work around them.

Start with Lesson 1!

Explore the full course →

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Published via Towards AI