Why Do We Spend So Much Time on Fundamentals?
Sharpening the axe doesn't delay the woodcutting · A solid foundation is the prerequisite for real engineering
Every AI engineering technique is, at its core, efficient management of context
Whether it's Prompt Engineering, RAG, Fine-tuning, or Agent tool-calling, virtually every technique revolves around processing this message list. Once you understand it, you can truly judge whether a solution is good and where a problem lies.
Prompt Engineering
Carefully crafting messages so the model sees the right context: system instructions, role definitions, and few-shot examples — all of it is inserting content into the message list.
RAG (Retrieval-Augmented Generation)
Fetching relevant document chunks from an external knowledge base and appending them to the message list before sending to the model — it's essentially expanding context at runtime.
Agent Tool-Calling
The model outputs a function call → executes the tool → appends the result back to the message list → reasons again. Each round accumulates more context.
Fine-tuning / SFT
Baking a large collection of ideal message lists into the model weights, so the model handles context the desired way by default — eliminating the need to explain it every time in the Prompt.
"Tweaking the Prompt doesn't help"
The System Prompt is being truncated or chat history is filling the window — the root cause is context management, nothing to do with how well the Prompt is written.
"RAG quality is poor — no idea which step is broken"
Chunking granularity, Embedding model choice, similarity threshold — without understanding the principles you don't know where to look, so you just trial-and-error blindly.
"The model got it wrong — should I fix the Prompt or Fine-tune?"
Is the context missing, or does the model simply lack that knowledge in its weights? The fixes are completely different, and going down the wrong path wastes enormous time.
I will spend considerable time on this section — I strongly recommend not skipping it.
Once you understand how the message list is processed, you'll instantly see through any engineering solution: what it does, why it works, and where its limits are.