The LLM Triangle Principles

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🧠 The LLM Triangle Principles

Large Language Models (LLMs) hold immense potential, but developing production-grade applications remains challenging. After building numerous LLM systems, I've identified 3+1 principles essential for success:

LLM Triangle Principles

LLM-Native apps are 10% sophisticated model and 90% data-driven engineering. Building reliable LLM applications requires careful engineering practices. When direct user interaction with the LLM is not possible, prompt composition must cover all nuances.

1. Standard Operating Procedure (SOP)

The SOP is a set of detailed, step-by-step instructions guiding the LLM like an inexperienced worker. This ensures consistent, high-quality results. Cognitive modeling helps create an effective SOP by mapping the thought processes of domain experts and breaking down tasks into manageable steps.

2. Engineering Techniques

Engineering techniques implement the SOP and maximize model performance. These include:

• LLM-Native Architectures: Define agentic flows to achieve tasks by integrating deterministic code and LLM agents.

  

• Agents: Standalone components using LLMs for specific tasks. Some agents use tools for calculations or searches, enhancing their autonomy but necessitating quality control measures.

3. Model Selection

Choosing the right model is crucial. Large models (e.g., GPT-4) offer better results but are costly, while smaller models are cost-effective. Key considerations include task complexity, infrastructure, pricing, latency, and data availability. Fine-tuning should be a last resort due to its complexity and cost.

4. Contextual Data

LLMs thrive on context. Providing relevant, well-structured data enhances performance without extensive retraining. Techniques include:

• Few-shot Learning: Guide models with representative examples.

• Retrieval Augmented Generation (RAG): Retrieve relevant documents to inform responses, keeping them current and factual.

Conclusion

The LLM Triangle Principles—Model, Engineering Techniques, and Contextual Data, guided by a clear SOP—provide a structured approach for developing high-quality, reliable LLM-native applications. Organizations can move beyond proofs-of-concept to robust, production-ready LLM solutions by applying these principles.

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