Where to Go Next: A Real Learning Path for Building with AI

Parts 1-3:   what AI, ML, and Deep Learning actually are, and the
               real history behind today's models
Parts 4-9:   how generation, neural networks, ChatGPT, tokens, and
               context windows actually work — the model itself
Parts 10-13: prompt engineering, message roles, real techniques, and
               hallucination — controlling the model's real behavior
Parts 14-15: AI APIs and reading real documentation — the practical
               interface to a hosted model
Parts 16-18: a real, complete, working Python application — setup,
               error handling, streaming, and memory
Parts 19-20: RAG and agents — real, more scalable and more capable
               patterns beyond what parts 16-18 built
Part 21:     a direct, honest roundup of common misconceptions

class SupportConversation:
    def __init__(self, plan_data: str, max_history_messages: int = 10):
        self.plan_data = plan_data
        self.history: list[dict] = []
        self.max_history_messages = max_history_messages

    def ask(self, user_question: str) -> str:
        self.history.append({"role": "user", "content": user_question})
        trimmed_history = self.history[-self.max_history_messages:]

        try:
            response = client.messages.create(
                model="claude-sonnet-5",
                max_tokens=1024,
                system=(
                    f"You are a customer support assistant for Bright "
                    f"Leaf Coffee. Real, current plan data:\n"
                    f"{self.plan_data}\n\nOnly answer using this real "
                    f"data. If you don't have the information needed, "
                    f"say so directly."
                ),
                messages=trimmed_history,
            )
            answer = response.content[0].text
        except (APIStatusError, APIConnectionError):
            answer = "We're having trouble responding right now — please try again shortly."

        self.history.append({"role": "assistant", "content": answer})
        return answer

RAG in practice: implementing part 19's retrieval step with a real
  vector database (this series covered the concept; a real
  implementation is a genuine next, more advanced topic)
Production agents: safely implementing part 20's multi-step tool use
  with real permission scoping and safeguards
Evaluation: systematically, real testing an AI application's output
  quality at scale, beyond the manual testing loop from part 10
Fine-tuning: genuinely adjusting a model's own parameters (part 6),
  for the narrower set of real cases where prompt engineering alone
  isn't sufficient

This series' own real project reuses Bright Leaf Coffee, the same
  business built throughout the Google Ads, Meta Ads, Content
  Marketing, and Google Analytics series — an AI support assistant
  is a genuinely real feature that same business would actually want,
  not a disconnected example