Optimizing LLM Accuracy - OpenAI API

Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's platform.

How to maximize correctness and consistent behavior when working with LLMs Optimizing LLMs is hard. We’ve worked with many developers across both start-ups and enterprises, and the reason optimization is hard consistently boils down to these reasons: Knowing how to start optimizing accuracy When to use what optimization method What level of accuracy is good enough for production This paper gives a mental model for how to optimize LLMs for accuracy and behavior. We’ll explore methods like prompt engineering, retrieval-augmented generation (RAG) and fine-tuning. We’ll also highlight how and when

related reading

• Patterns for Building LLM-based Systems & Productseugeneyan.com
• What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
• GitHub - brexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4.github.com
• Model optimization | OpenAI APIplatform.openai.com
• Parsed | Custom, interpretable AI systems that continuously learnparsed.com
• The Developer's Guide to Production-Grade LLM Appsbuildingaistuff.com
• Fine-Tuning Llama-2: Tailoring Models to Unique Applicationsanyscale.com
• LLM Evaluation doesn't need to be complicatedphilschmid.de
• What We Learned from a Year of Building with LLMs (Part I) – O’Reillyoreilly.com
• Productizing Large Language Modelsblog.replit.com
• PostTrainBenchposttrainbench.com
• Building LLM applications for productionhuyenchip.com