Core Essentials of LLM Applications | Generated by AI
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.