Maximizing Large Language Model Performance: A Survey of Techniques


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

Evaluating incrementally is critical - establish baseline, analyze errors, pick next technique deliberately based on gaps.
Quality over quantity - small, high-quality datasets beat larger, lower-quality ones for fine-tuning.
Optimization not linear - prompt, RAG, fine-tuning can combine over multiple cycles to solve complex problems.
RAG adds knowledge, fine-tuning changes behavior - pick technique based on specific gaps identified.
Prompt engineering is fastest experimentation - start there before investment in RAG or fine-tuning.
Fine-tuning distills and emphasizes existing knowledge - won't add brand new content.
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