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AI & ChatGPT Central
In todayâs edition we break down conversation design, show when to reach for retrieval over memory, and map a clean path into LangChain, agents, and evaluation. You will get a quick way to scope use cases, a simple RAG checklist, and a production-ready monitoring plan. This free book (worth 28.00$) is written for leaders who want results, not research papers. Read it, make one change in your flow, and watch handle time drop or self-serve rates rise. Hit reply and tell us what you shipped this week.
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đ©đ» Kris & The AI Central Team

Learn how to automate conversations —ïž
If you are starting fresh, begin with conversation design. Draft sample dialogues, personas, and guardrails first, then let the model fill the gaps. The book ChatGPT for Conversational AI and Chatbots lays out this order clearly and dedicates full chapters to LangChain, RAG, and production monitoring, plus a diagram of RLHF you can show your team (see Figure 1.5 and the chapters list for 5â9).
Two fresh notes for builders:
Treat LCEL as your default chain spec. LangChainâs Expression Language makes prompts, tools, and parsing composable and traceable, and it plugs straight into LangSmith. That means easier evals and fewer silent failures.
SLMs are a cost-control weapon. Microsoftâs Phi-3 shows how small models now punch above their weight on language, coding, and math while keeping latency and spend low. Use them for triage, classification, and routing to keep GPT-4 class models focused on high-value turns.
Shipping checklist: scope intents and success metrics, wire a retrieval layer before memory, build your chain in LCEL, add tool access only where needed, then evaluate with LangSmithâs off-the-shelf RAG evaluators and track cost-latency-quality daily.
Action: Pick one flow this week. Add RAG, log to LangSmith, and compare accuracy vs. your static prompt. If quality jumps, expand.

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