What We’ve Learned From A Year of Building with LLMs

2024-06-08 · Source: Hamel Husain's Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, extended

Summary

A guide titled "What We’ve Learned From A Year of Building with LLMs," authored by Eugene Yan, Bryan Bischof, Charles Frye, Hamel Husain, Jason Liu, and Shreya Shankar, was published on June 8, 2024. It provides practical insights for developing successful LLM products, categorizing lessons into tactical, operational, and strategic considerations. The tactical section covers prompting, RAG, flow engineering, evaluations, and monitoring. Operationally, it addresses data quality, model management, and team dynamics. Strategically, the guide discusses pretraining, finetuning, self-hosting, and the importance of building robust systems around models. It emphasizes that while LLMs are becoming more accessible and cost-effective, creating effective products beyond simple demos remains challenging, with an estimated $200 billion investment in AI by 2025.

Key takeaway

For AI Product Managers and Engineers aiming to scale LLM applications beyond initial demos, prioritize building robust evaluation and monitoring systems from the outset. Focus on creating a data flywheel through structured feedback loops and simplify annotation tasks to binary or pairwise comparisons to ensure high-quality data collection. This approach will enable faster iteration, more reliable product development, and better alignment with user needs, ultimately driving sustainable value.

Key insights

Successful LLM product development requires a holistic approach beyond demos, focusing on robust systems, data, and strategic iteration.

Principles

• Prioritize deterministic workflows for reliable agents.
• The system around the model provides lasting value.
• Start simple and add complexity only as needed.

Method

Begin with prompt engineering, then build specific evaluations and establish a data flywheel for continuous improvement and model refinement.

In practice

• Use n-shot prompts (n ≥ 5) and Chain-of-Thought for better performance.
• Implement hybrid search (keyword + embeddings) for RAG.
• Simplify human annotation to binary tasks or pairwise comparisons.

Topics

• Prompt Engineering
• Retrieval-Augmented Generation
• LLM Evaluation
• AI Product Strategy
• LLMOps

Code references

• jxnl/instructor
• outlines-dev/outlines
• wyim/aci-bench
• topics/pii-detection
• hamelsmu/ft-drift

Best for: AI Engineer, MLOps Engineer, AI Product Manager

Related on AIssential

• Your AI Product Needs Evals — Robust evaluation systems are critical for iterating quickly and improving LLM-powered AI products beyond initial demos.
• From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors — Integrating benchmark code examples into prompts significantly enhances LLM-driven algorithm optimization performance and robustness.
• LAI #117: Why Reliable AI Systems Are Still So Hard to Build — AI agent development requires robust engineering for reliability, observability, and controlled autonomy to move beyond hype.
• The Must-Know Topics for an LLM Engineer — LLM engineering requires understanding an interdependent stack, from data representation and model architecture to training, optimization, and evaluation.
• This Week in AI: Your Recap — Augmenting LLMs with advanced techniques like RAG and agentic workflows significantly enhances their real-world utility and precision.
• The “wow demo” trap is killing LLM projects. Here’s the exit. — Transitioning from LLM demos to production requires structured learning in architecture, reliability, and full-stack implementation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Hamel Husain's Blog.