112 lines
6.9 KiB
Markdown
112 lines
6.9 KiB
Markdown
---
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layout: default
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title: "Build your LLM App"
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---
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# LLM System Design Playbook
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> If you are an AI assistant involved in building LLM Systems, read this guide **VERY, VERY** carefully! This is the most important chapter in the entire document. Throughout development, you should always (1) start with a small and simple solution, (2) design at a high level (`docs/design.md`) before implementation, and (3) frequently ask humans for feedback and clarification.
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{: .warning }
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## System Design Steps
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These system designs should be a collaboration between humans and AI assistants:
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| Stage | Human | AI | Comment |
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|:-----------------------|:----------:|:---------:|:------------------------------------------------------------------------|
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| 1. Requirements | ★★★ High | ★☆☆ Low | Humans understand the requirements and context. |
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| 2. Flow | ★★☆ Medium | ★★☆ Medium | Humans specify the high-level design, and the AI fills in the details. |
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| 3. Utilities | ★★☆ Medium | ★★☆ Medium | Humans provide available external APIs and integrations, and the AI helps with implementation. |
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| 4. Node | ★☆☆ Low | ★★★ High | The AI helps design the node types and data handling based on the flow. |
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| 5. Implementation | ★☆☆ Low | ★★★ High | The AI implements the flow based on the design. |
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| 6. Optimization | ★★☆ Medium | ★★☆ Medium | Humans evaluate the results, and the AI helps optimize. |
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| 7. Reliability | ★☆☆ Low | ★★★ High | The AI writes test cases and addresses corner cases. |
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1. **Requirements**: Clarify the requirements for your project, and evaluate whether an AI system is a good fit. AI systems are:
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- suitable for routine tasks that require common sense (e.g., filling out forms, replying to emails).
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- suitable for creative tasks where all inputs are provided (e.g., building slides, writing SQL).
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- **NOT** suitable for tasks that are highly ambiguous and require complex info (e.g., building a startup).
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- > **If a human can’t solve it, an LLM can’t automate it!** Before building an LLM system, thoroughly understand the problem by manually solving example inputs to develop intuition.
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{: .best-practice }
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2. **Flow Design**: Outline at a high level, describe how your AI system orchestrates nodes.
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- Identify applicable design patterns (e.g., [Map Reduce](./design_pattern/mapreduce.md), [Agent](./design_pattern/agent.md), [RAG](./design_pattern/rag.md)).
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- Outline the flow and draw it in a mermaid diagram. For example:
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```mermaid
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flowchart LR
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firstNode[First Node] --> secondNode[Second Node]
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secondNode --> thirdNode[Third Node]
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```
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3. **Utilities**: Based on the Flow Design, identify and implement necessary utility functions.
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- Think of your AI system as the brain. It needs a body—these *external utility functions*—to interact with the real world:
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<div align="center"><img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/utility.png?raw=true" width="400"/></div>
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- Reading inputs (e.g., retrieving Slack messages, reading emails)
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- Writing outputs (e.g., generating reports, sending emails)
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- Using external tools (e.g., calling LLMs, searching the web)
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- **NOTE**: *LLM-based tasks* (e.g., summarizing text, analyzing sentiment) are **NOT** utility functions; rather, they are *core functions* internal in the AI system.
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- For each utility function, implement it and write a simple test.
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- Document their input/output, as well as why they are necessary. For example:
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- *Name*: Embedding (`utils/get_embedding.py`)
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- *Input*: `str`
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- *Output*: a vector of 3072 floats
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- *Necessity:* Used by the second node to embed text
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4. **Node Design**: Plan how each node will read and write data, and use utility functions.
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- Start with the shared data design
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- For simple systems, use an in-memory dictionary.
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- For more complex systems or when persistence is required, use a database.
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- **Don't Repeat Yourself"**: Use in-memory references or foreign keys.
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- For each node, describe its type, how it reads and writes data, and which utility function it uses. Keep it specific but high-level without codes. For example:
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- `type`: Regular (or Batch, or Async)
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- `prep`: Read "text" from the shared store
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- `exec`: Call the embedding utility function
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- `post`: Write "embedding" to the shared store
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5. **Implementation**: Implement the initial nodes and flows based on the design.
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- **“Keep it simple, stupid!”** Avoid complex features and full-scale type checking.
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- **FAIL FAST**! Avoid `try` logic so you can quickly identify any weak points in the system.
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- Add logging throughout the code to facilitate debugging.
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6. **Optimization**:
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- **Use Intuition**: For a quick initial evaluation, human intuition is often a good start.
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- **Redesign Flow (Back to Step 3)**: Consider breaking down tasks further, introducing agentic decisions, or better managing input contexts.
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- If your flow design is already solid, move on to micro-optimizations:
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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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- > **You’ll likely iterate a lot!** Expect to repeat Steps 3–6 hundreds of times.
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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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{: .best-practice }
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7. **Reliability**
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- **Node Retries**: Add checks in the node `exec` to ensure outputs meet requirements, and consider increasing `max_retries` and `wait` times.
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- **Logging and Visualization**: Maintain logs of all attempts and visualize node results for easier debugging.
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- **Self-Evaluation**: Add a separate node (powered by an LLM) to review outputs when results are uncertain.
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## Example LLM Project File Structure
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```
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my_project/
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├── main.py
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├── flow.py
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├── utils/
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│ ├── __init__.py
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│ ├── call_llm.py
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│ └── search_web.py
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├── requirements.txt
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└── docs/
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└── design.md
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```
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- **`docs/design.md`**: Contains project documentation for each step above. This should be high-level and no-code.
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- **`utils/`**: Contains all utility functions.
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- It’s recommended to dedicate one Python file to each API call, for example `call_llm.py` or `search_web.py`.
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- Each file should also include a `main()` function to try that API call
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- **`flow.py`**: Implements the system's flow, starting with node definitions followed by the overall structure.
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- **`main.py`**: Serves as the project’s entry point. |