docs(notes): add core LLM essentials guide

Author: lzwjava

_posts/en/2025-01-11-notes-en.md

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-These 2131 notes are primarily generated by AI chatbots. I used them to summarize key points and will walk through them to enhance my understanding.
+These 2132 notes are primarily generated by AI chatbots. I used them to summarize key points and will walk through them to enhance my understanding.
 
+* [Core Essentials of LLM Applications](/notes/2025-09-05-core-llm-essentials-en)
 * [AI Model Context Window Capacities](/notes/2025-09-03-ai-model-context-window-en)
 * [Alphabet Hits $186.66B in H1 2025](/notes/2025-09-03-alphabet-h1-2025-revenue-en)
 * [Building Trust Takes Time and Consistency](/notes/2025-09-03-building-trust-consistency-en)

notes/2025-09-05-core-llm-essentials-en.md

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+---
+title: Core Essentials of LLM Applications
+lang: en
+layout: post
+audio: false
+translated: false
+generated: true
+image: false
+---
+
+Yes, you’re thinking about it in the right direction. For most real-world applications built on LLMs (or any agent framework), the *essential information* comes down to a few fixed pieces. Everything else can usually be generated or scaffolded dynamically. Let me break it down:
+
+---
+
+### 1. **Customized or Vertical Agent Code**
+
+This is your application’s unique “glue.” It defines:
+
+* The **specialized logic** for your vertical (finance, healthcare, e-commerce, etc.).
+* The **integration points** (APIs, databases, internal tools).
+* The **workflow orchestration** (how the agent reacts step by step, what functions it calls, what state it keeps).
+
+This is what differentiates your app from a generic chatbot.
+
+---
+
+### 2. **Original Logs / Data**
+
+Your application’s **real data** is gold.
+
+* Transaction logs, knowledge bases, user histories, product catalogs, etc.
+* This is the source of truth that makes your agent contextually relevant and useful.
+
+The more structured and trustworthy this data is, the stronger your application becomes.
+
+---
+
+### 3. **Prompt and Context**
+
+This is where you tell the model:
+
+* **How to think**: rules, tone, guardrails, task-specific instructions.
+* **What to see**: injecting relevant context (from logs, user query, memory, or retrieval).
+
+Prompts are the “interface” between the raw model and your customized code/data.
+
+---
+
+### 4. **Everything Else Can Be Generated**
+
+UI scaffolding, boilerplate templates, summaries, natural-language expansions, even parts of the integration code — these can often be generated automatically or semi-automatically. The differentiators (logic, data, prompt/context design) are the parts you can’t outsource to generation alone.
+
+---
+
+👉 So your summary is on point:
+The **essentials** are (1) your agent logic/code, (2) your real logs/data, and (3) the prompt/context shaping. The rest (like response formatting, helper text, or generic flows) can indeed be generated.
+
+---
+
+Would you like me to sketch a **minimal stack diagram** that shows these layers visually (essential vs. generatable)? That might make the separation clearer.

original/2025-01-11-notes-en.md

@@ -7,8 +7,9 @@ generated: false
 translated: false
 ---
 
-These 2131 notes are primarily generated by AI chatbots. I used them to summarize key points and will walk through them to enhance my understanding.
+These 2132 notes are primarily generated by AI chatbots. I used them to summarize key points and will walk through them to enhance my understanding.
 
+* [Core Essentials of LLM Applications](/notes/2025-09-05-core-llm-essentials-en)
 * [AI Model Context Window Capacities](/notes/2025-09-03-ai-model-context-window-en)
 * [Alphabet Hits $186.66B in H1 2025](/notes/2025-09-03-alphabet-h1-2025-revenue-en)
 * [Building Trust Takes Time and Consistency](/notes/2025-09-03-building-trust-consistency-en)