Why so many LLM projects fail before they begin
The missing link: LLM Training (part 1)!
Good morning, AI enthusiasts!
We just released something that’s been a long time coming:
A free, no-fluff session on how LLMs actually work—meant for developers, builders, and technically curious folks who are tired of guessing.
You know the feeling.
You’ve copied the RAG stack. You’ve tuned the prompt. You’ve played with agents. But under the hood, a lot of it still feels like magic—or worse, like a black box you hope doesn’t break.
We wanted to change that.
This first session of our LLM Developer Primer breaks down:
What LLMs are really doing under the hood (from tokens to attention to output)
The five most common failure modes (and how to spot them early)
What makes LLMs genuinely useful—and why plug-and-play often isn’t enough
Why most current tooling assumes mental models that most devs don’t yet have
No hype. No hand-waving. Just a clear, structured walkthrough to help you reason about LLMs before you go too deep with the wrong assumptions.
If it lands, and you want more, we’ve expanded it into a full 10-hour course covering the full LLM application pipeline.
There are a lot of AI courses out there right now, but most either stop at surface-level tooling or dive deep into academic theory with little concern for practical application. We wanted to build the missing middle.
The full course goes into:
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
The course is currently $199, and includes lifetime access + a free update when we release our upcoming 2-hour deep dive on fine-tuning open models.