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---
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layout: default
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title: "Essay"
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parent: "Apps"
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nav_order: 2
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---
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# Summarization + QA agent for Paul Graham Essay
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# Summarization + QA agent for Paul Graham Essay
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```python
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```python
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---
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---
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layout: default
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layout: default
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title: "Design Guidance"
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title: "Development Playbook"
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parent: "Apps"
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parent: "Apps"
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nav_order: 1
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nav_order: 1
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---
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---
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# LLM System Design Guidance
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# LLM Application Development Playbook
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## System Design Steps
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## System Design Steps
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- **Prompt Engineering**: Use clear, specific instructions with examples to reduce ambiguity.
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- **Prompt Engineering**: Use clear, specific instructions with examples to reduce ambiguity.
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- **In-Context Learning**: Provide robust examples for tasks that are difficult to specify with instructions alone.
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- **In-Context Learning**: Provide robust examples for tasks that are difficult to specify with instructions alone.
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- > **You’ll likely iterate repeatedly!** Expect to repeat Steps 3–6 hundreds of times.
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- > **You’ll likely iterate a lot!** Expect to repeat Steps 3–6 hundreds of times.
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>
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>
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> <div align="center"><img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/success.png?raw=true" width="400"/></div>
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> <div align="center"><img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/success.png?raw=true" width="400"/></div>
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{: .best-practice }
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{: .best-practice }
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@ -59,9 +59,4 @@ We model the LLM workflow as a **Nested Directed Graph**:
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- [(Advanced) Multi-Agents](./multi_agent.md)
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- [(Advanced) Multi-Agents](./multi_agent.md)
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- Evaluation
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- Evaluation
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## Example LLM Apps
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## [LLM Application Development Playbook](./guide.md)
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[LLM System Design Guidance](./guide.md)
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- [Summarization + QA agent for Paul Graham Essay](./essay.md)
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- More coming soon...
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