16. What’s Next?

16. What’s Next?#

Congratulations on completing LLMs from Scratch! You’ve journeyed from the atomic units of language models—tokens—all the way to alignment techniques and cutting-edge architectures. This notebook recaps what you’ve learned and points you toward the next frontier: building intelligent agents.

Key Takeaways#

  1. Tokenization matters: The compression ratio and vocabulary design directly impact model efficiency and capability.

  2. Architecture evolution: From vanilla attention to MLA + MoE, each advancement addresses specific bottlenecks (memory, compute, expressivity).

  3. Data quality > quantity: Well-curated data often outperforms larger but noisier datasets.

  4. Scaling is predictable: Chinchilla laws let you budget compute optimally before training.

  5. Alignment is essential: SFT + RLHF transforms a text predictor into a helpful assistant.

  6. Efficiency enables deployment: Quantization, pruning, and PEFT make large models practical.

What’s Next? From LLMs to Agents#

You now understand how to build, train, and optimize LLMs. But the real power emerges when you turn these models into autonomous agents that can:

  • Use tools: Call APIs, execute code, query databases

  • Plan and reason: Break complex tasks into steps and self-correct

  • Maintain memory: Remember context across long interactions

  • Collaborate: Work with other agents in multi-agent systems

Ready to Build Your Own Super Agents?#

If you want to dive deeper into building production-ready AI agents that leverage the LLM foundations you’ve learned here, check out my next course:

Build Your Own Super Agents#

In this course, you’ll learn:

  • Agent architectures: ReAct, Plan-and-Execute, and custom reasoning loops

  • Tool use and function calling: Integrating external capabilities into your agents

  • Memory systems: Short-term, long-term, and retrieval-augmented memory

  • Multi-agent orchestration: Building teams of specialized agents

  • Production deployment: Scaling, monitoring, and safety guardrails

Thank You!#

Thank you for taking this journey through the internals of large language models. The best way to learn LLMs is to build one—and you’ve done exactly that.

Now go build something amazing!!

— Shreshth Tuli

Website | LinkedIn | GitHub

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