Maximizing Large Language Model Performance: A Survey of Techniques

Summary

There are various techniques that can be used to optimize the performance of large language models on specific tasks, including prompt engineering to provide clear instructions, retrieval-augmented generation to bring in relevant external knowledge, and fine-tuning to adapt models to new domains. Evaluating performance at each step is key.

Timestamped Highlights

💡 Introducing key innovations in OpenAI's fine-tuning capabilities over recent months, like Turbo fine-tuning and continuous fine-tuning.
💡 Framework for context optimization vs. LLM optimization. Prompt engineering provides baseline, RAG brings knowledge, fine-tuning changes model behavior.
💡 Prompt engineering limitations: can't easily introduce new info, replicate complex style/method, minimize token usage.
💡 RAG good for: introducing new information, reducing hallucinations. Not good for: teaching broad domains or new languages/styles, reducing tokens.
💡 Example case reaching 98% accuracy using RAG without any fine-tuning, by carefully evaluating and iterating retrieval quality.
💡 Key fine-tuning benefits: reach otherwise impossible performance levels, create more efficient models.
💡 Fine-tuning + RAG combines customized model with maximal relevant context.

Key Insights

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Evaluating incrementally is critical - establish baseline, analyze errors, pick next technique deliberately based on gaps.
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Quality over quantity - small, high-quality datasets beat larger, lower-quality ones for fine-tuning.
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Optimization not linear - prompt, RAG, fine-tuning can combine over multiple cycles to solve complex problems.
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RAG adds knowledge, fine-tuning changes behavior - pick technique based on specific gaps identified.
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Prompt engineering is fastest experimentation - start there before investment in RAG or fine-tuning.
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Fine-tuning distills and emphasizes existing knowledge - won't add brand new content.
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Combined RAG + fine-tuning maximizes performance and efficiency.
This blog is a summary of a YouTube video "A Survey of Techniques for Maximizing LLM Performance - YouTube" by OpenAI