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LLM Performance Optimization Techniques

Haebom
This is an LLM performance optimization method unveiled at OpenAI's recent DevDay.
The process of optimizing LLMs is not straightforward and requires an experimental approach along with repeated evaluation.
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At first, a baseline is set through Prompt Engineering, then RAG and fine-tuning are applied as needed.
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Through these steps, efficient model performance is achieved, and the right optimization technique is applied depending on the type of problem.

Prompt Engineering

Write clear instructions and break down complex tasks into simple subtasks. Lately, lots of people keep talking about making prompts long and detailed... but in theory, providing consistent, clear, and concise instructions actually results in better reasoning.
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At this stage, it's important to give clear and concise instructions to minimize token usage.
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When complex logical reasoning is required, make sure to give the model enough time to think.

Retrieval-Augmented Generation (RAG)

You can expand tasks by accessing reference texts or external tools. With the arrival of GPTs, RAG has become even more important. Moving forward, not just OpenAI, but also tools from Google like Makesaker are expected to actively embrace this approach.
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RAG updates a model's knowledge by supplying it with new information.
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This technique is also used to ensure the model only uses trusted content.

Fine-tuning

Provide consistent instructions to the model and emphasize the specialized knowledge needed to solve your problem. This is probably so well known that it hardly needs more explanation.
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Fine-tuning is a technique where you further train an existing model on a domain-specific dataset.
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This can help boost a model’s performance and efficiency, and since there are fewer constraints on data volume, it can maximize performance.

Model evaluation?

Lately, it's becoming common to see small or forked models trained specifically on evaluation datasets just to boost their scores. Not only is this unethical, it can also negatively affect how other models are evaluated.
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When evaluating a model's performance, we consider metrics like accuracy, consistency, and appropriateness of the response.
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When using a RAG model, content suitability is another key factor to consider.
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For OpenAI's GPT-4, in the case where 98% accuracy was achieved, the model was improved through re-ranking, rule-based approaches, classification, and other methods. These examples show that with the right context and selection, you can solve problems even without fine-tuning.
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