Model I/O
Raw API chatbot with streaming, typed output, a tool loop, retries, and cost logs.
A complete, text-first twelve-week field course: concepts, architecture diagrams, runnable labs, evaluation gates, debugging notes, and interview evidence—without a video syllabus.
You do not need to relearn software engineering. Add the probabilistic layer, then make it observable, evaluable, and safe.
Every phase ends with something inspectable. The strongest portfolio is not four chat UIs; it is four measured systems with failure analysis.
Raw API chatbot with streaming, typed output, a tool loop, retries, and cost logs.
Document QA with dense + lexical retrieval, reranking, citations, and a held-out test set.
Bounded stateful agent with explicit transitions, approval gates, tracing, and MCP.
Small LoRA/QLoRA experiment with a data card and before/after behavioral eval.
Golden dataset, regression gate, adversarial suite, and failure taxonomy.
Load-tested inference path, routing policy, cost model, runbook, and design narrative.
Begin with raw model calls, a tiny dataset, and a visible trace. Frameworks become valuable only when the repetition is understood.
A fluent wrong answer may come from bad search, bad context use, or a bad source. Score the stages independently.
Use deterministic code for rules, the model for judgment, and explicit approval for consequential actions.
A graph of quality, latency, and cost plus three analyzed failures is stronger than another polished chatbot shell.
Try to answer aloud, then reveal. The point is to rehearse engineering decisions, not terminology.
The lessons are self-contained; these primary papers and official docs support the claims and let you inspect the originals when you want to go deeper.