Real implementations, real bugs, and the mental models behind building on LLMs. I'm a fullstack engineer with 10+ years of experience, including 4 years at Apple, now going deep on applied AI: streaming, RAG, tool use, agents, and the full stack of tooling modern AI products are built on.
Every module up to this point used the same streaming events without ever reading two fields that were there the entire time. message_start and message_delta carry token usage on every single stream —
Everything in the earlier modules assumed the happy path: the API responds, the model generates valid output, tools return results, the stream closes cleanly. In production, none of that is guaranteed
Phase 4 gave the study system memory of individual problems — when each one is due for review based on SM-2. Phase 5 asks a different question: zoomed out across all topics, what should I actually be
04-structured-output solved the reliability problem: use forced tool use to guarantee valid JSON back from the model. But it waited for the complete response before doing anything with it. For a backe
The first time I ran evals on my LLM playground, the agent scored lower than basic RAG — 7.5 vs 8.9. The agent is more capable: it has live tool data on top of retrieval, it can answer questions basic
The first three phases of my study system handled the session loop: generate a problem, grade the solution, log the result. Useful, but with a fundamental gap — there was no memory of what I struggled
In the basic RAG module, retrieval was unconditional. Every query triggered the same pipeline: embed the query, search the vector store, stuff the top chunks into the prompt, generate. The model had n
Picking up Python as a JavaScript engineer is mostly straightforward because the mental models transfer well. Functions, modules, async patterns, data structures — the concepts are the same. What trip
Prompt engineering only becomes interesting when you're prompting for structured, specific output that has to be reliable enough to feed downstream systems. A chatbot that gives a slightly different a
