Why so many LLM projects fail before they begin
The missing link: LLM Training (part 1)!
We’ve noticed a pattern.
A developer wants to build with LLMs. They start with the tools: try a RAG template, play with LangChain, maybe even jump to fine-tuning.
But something feels off. The outputs are shaky. Latency spikes. Evaluating results feels like guesswork. And it’s hard to explain why something works—until it doesn’t.
It’s not a tooling problem.
It’s a mental model problem.
Most devs are jumping into LLM workflows without understanding what’s actually happening under the hood. And without that foundation, even the most hyped-up stack won’t help.
So we made the thing we wish we had:
A clear, practical breakdown of how LLMs generate, reason, and fail—in language built for people who ship.
You can now watch the first session of our LLM Developer Primer for free.
In this first session, we break down:
How LLMs generate outputs step by step—tokenization, embeddings, attention, autoregression
Where and why they fail: hallucinations, bias, outdated data, context length issues, logical fragility
Why prompt engineering works (sometimes), and when you need more serious intervention
The foundational risks of building with LLMs in production, like prompt injection, data leakage, and cascading failures from unpredictable completions
It’s not a fluffy overview or another marketing webinar. It’s what we wish we had when we were first trying to build serious systems.
If that clicks, the full 10-hour course goes deeper—way deeper.
You’ll walk through:
Designing end-to-end LLM pipelines, including RAG, prompt architectures, and task-specific fine-tuning
Evaluating LLMs with automated metrics (BLEU, ROUGE, perplexity) and human-in-the-loop testing
Understanding agent workflows, tool use, orchestration, and how to manage cost/latency trade-offs
Applying core optimization and safety practices like quantization, distillation, RLHF, and injection mitigation
By the end, you’ll know how to build, evaluate, automate, and maintain LLM systems that hold up in production, not just on a notebook.
“Outstanding resource to master LLM development.”
“Helped me debug and design with confidence.”
“Gave me the mental model I didn’t know I was missing.”
Start with the free session → Watch it here
And if you’re ready to go from trial-and-error to informed decisions, the full course is available now at launch pricing ($199.99).